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macroplacement
Unverified Commit 9d08572f by Yucheng Wang Committed by GitHub
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Merge pull request #17 from TILOS-AI-Institute/plc_client-open-source 

Plc client open source
parents 7ad47f7c fc82171e
Showing with 8539 additions and 98 deletions
CodeElements/Plc_client/README.md
......@@ -27,8 +27,50 @@ $$
HPWL(netlist) = \sum_{i}^{N_{netlist}} W_{i\_{source}} \cdot [max_{b\in i}(x_b) - min_{b\in i}(x_b) + max_{b\in i}(y_b) - min_{b\in i}(y_b)]
$$
## Density Cost Computation
Density cost is computed from grid cells density.
By default, any given input will have grid col/row set to 10/10 until user later defines in the .plc file.
Grid cell density is represented as an 1D array where the length is set to be
$$
grid_\{col} \cdot grid_\{row}
$$
Each entry of this array represents the current occupied precentage within this cell.
Overlaps gets double counted, so it is possible for a cell density to exceed 1.0.
When a Placement Cost object is initialized, coordinate information and dimension information will be read from the netlist input and used to compute an initial grid cell density.
Once grid cell density is obtained, we take the top 10% of the largest grid cells and compute its average. The density cost will be half of the average value.
## Adjacency Matrix Computation
The adjacency matrix is represented as an array of $[N_{hardmacros} + N_{softmacros} + N_{ports}] \times [N_{hardmacros} + N_{softmacros} + N_{ports}]$ elements.
For each entry of the adjacency matrix, it represents the total number of connections between module $i$ and module $j$ subject to all corresponding pins.
The adjacency matrix is represented as an array of
$$
[N_{hardmacros} + N_{softmacros} + N_{ports}] \times [N_{hardmacros} + N_{softmacros} + N_{ports}]
$$
elements.
For each entry of the adjacency matrix, it represents the total number of connections between module $i$ and module $j$ subject to all corresponding pins. For soft macro pins, weight should be consider a factor to the number of connections.
## Congestion Cost Computation
Congestion cost is computed from the horizontal congestion grid cells array and the vertical congestion grid cells array. After concatenating these two arrays, Google takes the top 5% of the most congested grid cells and compute the average of them to get the final congestion cost.
To compute the congestion for each grid cell, a fast routing technique is applied which is detailed [here](https://docs.google.com/document/d/1hM7UbmANkhoGB3-UfFBp8TRDvvVjpmio7cyyjK4a5bI/edit?usp=sharing). After fast routing, we acquire the horizontal/vertical congestion due to net routing. This will be added with horiztonal/vertical congestion due to macro placement.
On a high level, for each grid cell, we compute the following:
$$
\text{Grid horizontal congestion = horizontal congestion due to macros + horizontal congestion due to net routing after smoothing.}
$$
$$
\text{Grid vertical congestion = vertical congestion due to macros + vertical congestion due to net routing after smoothing.}
$$
Notice a smoothing range can be set for congestion. This is only applied to congestion due to net routing which by counting adjacent cells and adding the averaged congestion to these adjacent cells. More details are provided in the document above.
CodeElements/Plc_client/plc_client_os.py
"""Open-Sourced PlacementCost client class."""
import os, io
from platform import node
import re
import math
from typing import Text, Tuple
from absl import logging
from collections import namedtuple
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import numpy as np
# FLAGS = flags.FLAG
Block = namedtuple('Block', 'x_max y_max x_min y_min')
class PlacementCost(object):
......@@ -26,10 +32,6 @@ class PlacementCost(object):
self.init_plc = None
self.project_name = "circuit_training"
self.block_name = netlist_file.rsplit('/', -1)[-2]
self.width = 0.0
self.height = 0.0
self.grid_col = 0
self.grid_row = 0
self.hroutes_per_micron = 0.0
self.vroutes_per_micron = 0.0
self.smooth_range = 0.0
......@@ -37,6 +39,12 @@ class PlacementCost(object):
self.hrouting_alloc = 0.0
self.vrouting_alloc = 0.0
self.macro_horizontal_routing_allocation = 0.0
self.macro_vertical_routing_allocation = 0.0
# net information
self.net_cnt = 0
# All modules look-up table
self.modules = []
self.modules_w_pins = []
......@@ -54,6 +62,21 @@ class PlacementCost(object):
self.soft_macros_to_inpins = {}
# read netlist
self.__read_protobuf()
# default canvas width/height based on cell area
self.width = math.sqrt(self.get_area()/0.6)
self.height = math.sqrt(self.get_area()/0.6)
# default gridding
self.grid_col = 10
self.grid_row = 10
# initialize congestion map
# TODO recompute after new gridding
self.V_routing_cong = [0] * self.grid_col * self.grid_row
self.H_routing_cong = [0] * self.grid_col * self.grid_row
self.V_macro_routing_cong = [0] * self.grid_col * self.grid_row
self.H_macro_routing_cong = [0] * self.grid_col * self.grid_row
# initial grid mask
self.global_node_mask = [0] * self.grid_col * self.grid_row
# store module/component count
self.port_cnt = len(self.port_indices)
self.hard_macro_cnt = len(self.hard_macro_indices)
......@@ -189,11 +212,16 @@ class PlacementCost(object):
x = attr_dict['x'][1],
y = attr_dict['y'][1],
macro_name = attr_dict['macro_name'][1])
if 'weight' in attr_dict.keys():
soft_macro_pin.set_weight(float(attr_dict['weight'][1]))
if input_list:
if 'weight' in attr_dict.keys():
self.net_cnt += 1 * float(attr_dict['weight'][1])
else:
self.net_cnt += 1
soft_macro_pin.add_sinks(input_list)
if 'weight' in attr_dict.keys():
soft_macro_pin.set_weight(float(attr_dict['weight'][1]))
self.modules_w_pins.append(soft_macro_pin)
# mapping node_name ==> node idx
......@@ -242,6 +270,10 @@ class PlacementCost(object):
hard_macro_pin.set_weight(float(attr_dict['weight'][1]))
if input_list:
if 'weight' in attr_dict.keys():
self.net_cnt += 1 * float(attr_dict['weight'][1])
else:
self.net_cnt += 1
hard_macro_pin.add_sinks(input_list)
self.modules_w_pins.append(hard_macro_pin)
......@@ -268,7 +300,9 @@ class PlacementCost(object):
side = attr_dict['side'][1])
if input_list:
self.net_cnt += 1
port.add_sinks(input_list)
# ports does not have pins so update connection immediately
port.add_connections(input_list)
self.modules_w_pins.append(port)
......@@ -331,13 +365,23 @@ class PlacementCost(object):
for pin_name in pin_names:
pin = self.modules_w_pins[self.mod_name_to_indices[pin_name]]
inputs = pin.get_sink()
if inputs:
for k in inputs.keys():
macro.add_connections(inputs[k])
if macro_type == "MACRO" or macro_type == "macro":
weight = pin.get_weight()
macro.add_connections(inputs[k], weight)
def __update_placement(self):
# assign modules to grid cells
pass
def get_cost(self) -> float:
return 0.0
"""
Compute wirelength cost from wirelength
"""
return self.get_wirelength() / ((self.get_canvas_width_height()[0] + self.get_canvas_width_height()[1]) * self.net_cnt)
def get_area(self) -> float:
"""
......@@ -383,15 +427,15 @@ class PlacementCost(object):
# add source position
x_coord.append(mod.get_pos()[0])
y_coord.append(mod.get_pos()[1])
for sink_name in mod.get_sink():
# retrieve indx in modules_w_pins
sink_idx = self.mod_name_to_indices[sink_name]
# retrieve sink object
sink = self.modules_w_pins[sink_idx]
# retrieve location
x_coord.append(sink.get_pos()[0])
y_coord.append(sink.get_pos()[1])
for sink_pin in mod.get_sink()[sink_name]:
# retrieve indx in modules_w_pins
sink_idx = self.mod_name_to_indices[sink_pin]
# retrieve sink object
sink = self.modules_w_pins[sink_idx]
# retrieve location
x_coord.append(sink.get_pos()[0])
y_coord.append(sink.get_pos()[1])
elif curr_type == "macro_pin" or curr_type == "MACRO_PIN":
# add source position
x_coord.append(mod.get_pos()[0])
......@@ -409,6 +453,7 @@ class PlacementCost(object):
# retrieve location
x_coord.append(input.get_pos()[0])
y_coord.append(input.get_pos()[1])
if x_coord:
if norm_fact != 1.0:
total_hpwl += norm_fact * \
......@@ -417,14 +462,224 @@ class PlacementCost(object):
else:
total_hpwl += (abs(max(x_coord) - min(x_coord))\
+ abs(max(y_coord) - min(y_coord)))
return total_hpwl
def get_congestion_cost(self) -> float:
return 0.0
def abu(self, xx, n = 0.1):
xxs = sorted(xx, reverse = True)
cnt = math.floor(len(xxs)*n)
if cnt == 0:
return max(xxs)
return sum(xxs[0:cnt])/cnt
def get_V_congestion_cost(self) -> float:
"""
compute average of top 10% of grid cell cong and take half of it
"""
occupied_cells = sorted([gc for gc in self.V_routing_cong if gc != 0.0], reverse=True)
cong_cost = 0.0
# take top 10%
cong_cnt = math.floor(len(self.V_routing_cong) * 0.1)
# if grid cell smaller than 10, take the average over occupied cells
if len(self.V_routing_cong) < 10:
cong_cost = float(sum(occupied_cells) / len(occupied_cells))
return cong_cost
idx = 0
sum_cong = 0
# take top 10%
while idx < cong_cnt and idx < len(occupied_cells):
sum_cong += occupied_cells[idx]
idx += 1
return float(sum_cong / cong_cnt)
def get_H_congestion_cost(self) -> float:
"""
compute average of top 10% of grid cell cong and take half of it
"""
occupied_cells = sorted([gc for gc in self.H_routing_cong if gc != 0.0], reverse=True)
cong_cost = 0.0
# take top 10%
cong_cnt = math.floor(len(self.H_routing_cong) * 0.1)
# if grid cell smaller than 10, take the average over occupied cells
if len(self.H_routing_cong) < 10:
cong_cost = float(sum(occupied_cells) / len(occupied_cells))
return cong_cost
idx = 0
sum_cong = 0
# take top 10%
while idx < cong_cnt and idx < len(occupied_cells):
sum_cong += occupied_cells[idx]
idx += 1
return float(sum_cong / cong_cnt)
def get_congestion_cost(self):
#return max(self.get_H_congestion_cost(), self.get_V_congestion_cost())
return self.abu(self.V_routing_cong + self.H_routing_cong, 0.05)
# temp_cong = [sum(x) for x in zip(self.V_routing_cong, self.H_routing_cong)]
# occupied_cells = sorted([gc for gc in temp_cong if gc != 0.0], reverse=True)
# cong_cost = 0.0
# # take top 10%
# cong_cnt = math.floor(len(temp_cong) * 0.1)
# # if grid cell smaller than 10, take the average over occupied cells
# if len(temp_cong) < 10:
# cong_cost = float(sum(occupied_cells) / len(occupied_cells))
# return cong_cost
# idx = 0
# sum_cong = 0
# # take top 10%
# while idx < cong_cnt and idx < len(occupied_cells):
# sum_cong += occupied_cells[idx]
# idx += 1
# return float(sum_cong / cong_cnt)
def __get_grid_cell_location(self, x_pos, y_pos):
"""
private function for getting grid cell row/col ranging from 0...N
"""
row = math.floor(y_pos / self.grid_height)
col = math.floor(x_pos / self.grid_width)
return row, col
def __overlap_area(self, block_i, block_j):
"""
private function for computing block overlapping
"""
x_diff = min(block_i.x_max, block_j.x_max) - max(block_i.x_min, block_j.x_min)
y_diff = min(block_i.y_max, block_j.y_max) - max(block_i.y_min, block_j.y_min)
if x_diff >= 0 and y_diff >= 0:
return x_diff * y_diff
return 0
def __overlap_dist(self, block_i, block_j):
"""
private function for computing block overlapping
"""
x_diff = min(block_i.x_max, block_j.x_max) - max(block_i.x_min, block_j.x_min)
y_diff = min(block_i.y_max, block_j.y_max) - max(block_i.y_min, block_j.y_min)
if x_diff > 0 and y_diff > 0:
return x_diff, y_diff
return 0, 0
def __add_module_to_grid_cells(self, mod_x, mod_y, mod_w, mod_h):
"""
private function for add module to grid cells
"""
# Two corners
ur = (mod_x + (mod_w/2), mod_y + (mod_h/2))
bl = (mod_x - (mod_w/2), mod_y - (mod_h/2))
# construct block based on current module
module_block = Block(
x_max=mod_x + (mod_w/2),
y_max=mod_y + (mod_h/2),
x_min=mod_x - (mod_w/2),
y_min=mod_y - (mod_h/2)
)
# Only need two corners of a grid cell
ur_row, ur_col = self.__get_grid_cell_location(*ur)
bl_row, bl_col = self.__get_grid_cell_location(*bl)
# check if out of bound
if ur_row >= 0 and ur_col >= 0:
if bl_row < 0:
bl_row = 0
if bl_col < 0:
bl_col = 0
else:
# OOB, skip module
return
if bl_row >= 0 and bl_col >= 0:
if ur_row > self.grid_row - 1:
ur_row = self.grid_row - 1
if ur_col > self.grid_col - 1:
ur_col = self.grid_col - 1
else:
# OOB, skip module
return
for r_i in range(bl_row, ur_row + 1):
for c_i in range(bl_col, ur_col + 1):
# construct block based on current cell row/col
grid_cell_block = Block(
x_max= (c_i + 1) * self.grid_width,
y_max= (r_i + 1) * self.grid_height,
x_min= c_i * self.grid_width,
y_min= r_i * self.grid_height
)
self.grid_occupied[self.grid_col * r_i + c_i] += \
self.__overlap_area(grid_cell_block, module_block)
def get_grid_cells_density(self):
"""
compute density for all grid cells
"""
# by default grid row/col is 10/10
self.grid_width = float(self.width/self.grid_col)
self.grid_height = float(self.height/self.grid_row)
grid_area = self.grid_width * self.grid_height
self.grid_occupied = [0] * (self.grid_col * self.grid_row)
self.grid_cells = [0] * (self.grid_col * self.grid_row)
for module_idx in (self.soft_macro_indices + self.hard_macro_indices):
# extract module information
module = self.modules_w_pins[module_idx]
module_h = module.get_height()
module_w = module.get_width()
module_x, module_y = module.get_pos()
self.__add_module_to_grid_cells(
mod_x=module_x,
mod_y=module_y,
mod_h=module_h,
mod_w=module_w
)
for i, gcell in enumerate(self.grid_occupied):
self.grid_cells[i] = gcell / grid_area
return self.grid_cells
def get_density_cost(self) -> float:
pass
"""
compute average of top 10% of grid cell density and take half of it
"""
occupied_cells = sorted([gc for gc in self.grid_cells if gc != 0.0], reverse=True)
density_cost = 0.0
# take top 10%
density_cnt = math.floor(len(self.grid_cells) * 0.1)
# if grid cell smaller than 10, take the average over occupied cells
if len(self.grid_cells) < 10:
density_cost = float(sum(occupied_cells) / len(occupied_cells))
return 0.5 * density_cost
idx = 0
sum_density = 0
# take top 10%
while idx < density_cnt and idx < len(occupied_cells):
sum_density += occupied_cells[idx]
idx += 1
return 0.5 * float(sum_density / density_cnt)
def set_canvas_size(self, width:float, height:float) -> float:
"""
......@@ -432,6 +687,9 @@ class PlacementCost(object):
"""
self.width = width
self.height = height
self.grid_width = float(self.width/self.grid_col)
self.grid_height = float(self.height/self.grid_row)
return True
def get_canvas_width_height(self) -> Tuple[float, float]:
......@@ -446,6 +704,14 @@ class PlacementCost(object):
"""
self.grid_col = grid_col
self.grid_row = grid_row
self.V_routing_cong = [0] * self.grid_col * self.grid_row
self.H_routing_cong = [0] * self.grid_col * self.grid_row
self.V_macro_routing_cong = [0] * self.grid_col * self.grid_row
self.H_macro_routing_cong = [0] * self.grid_col * self.grid_row
self.grid_width = float(self.width/self.grid_col)
self.grid_height = float(self.height/self.grid_row)
return True
def get_grid_num_columns_rows(self) -> Tuple[int, int]:
......@@ -489,7 +755,7 @@ class PlacementCost(object):
"""
Set congestion smooth range
"""
self.smooth_range = smooth_range
self.smooth_range = int(smooth_range)
def get_congestion_smooth_range(self) -> float:
"""
......@@ -525,11 +791,404 @@ class PlacementCost(object):
"""
return self.hrouting_alloc, self.vrouting_alloc
def is_node_soft_macro(self) -> bool:
return True
def __two_pin_net_routing(self, source_gcell, node_gcells, weight):
temp_gcell = list(node_gcells)
if temp_gcell[0] == source_gcell:
sink_gcell = temp_gcell[1]
else:
sink_gcell = temp_gcell[0]
# y
row_min = min(sink_gcell[0], source_gcell[0])
row_max = max(sink_gcell[0], source_gcell[0])
# x
col_min = min(sink_gcell[1], source_gcell[1])
col_max = max(sink_gcell[1], source_gcell[1])
# H routing
for col_idx in range(col_min, col_max, 1):
col = col_idx
row = source_gcell[0]
self.H_routing_cong[row * self.grid_col + col] += weight
# V routing
for row_idx in range(row_min, row_max, 1):
row = row_idx
col = sink_gcell[1]
self.V_routing_cong[row * self.grid_col + col] += weight
def l_routing(self, node_gcells, weight):
node_gcells.sort(key = lambda x: (x[1], x[0]))
y1, x1 = node_gcells[0]
y2, x2 = node_gcells[1]
y3, x3 = node_gcells[2]
# H routing (x1, y1) to (x2, y1)
for col in range(x1, x2):
row = y1
self.H_routing_cong[row * self.grid_col + col] += weight
# H routing (x2, y2) to (x2, y3)
for col in range(x2,x3):
row = y2
self.H_routing_cong[row * self.grid_col + col] += weight
# V routing (x2, min(y1, y2)) to (x2, max(y1, y2))
for row in range(min(y1, y2), max(y1, y2)):
col = x2
self.V_routing_cong[row * self.grid_col + col] += weight
# V routing (x3, min(y2, y3)) to (x3, max(y2, y3))
for row in range(min(y2, y3), max(y2, y3)):
col = x3
self.V_routing_cong[row * self.grid_col + col] += weight
return
def t_routing(self, node_gcells, weight):
node_gcells.sort()
#print(node_gcells)
y1, x1 = node_gcells[0]
y2, x2 = node_gcells[1]
y3, x3 = node_gcells[2]
xmin = min(x1, x2, x3)
xmax = max(x1, x2, x3)
# H routing (xmin, y2) to (xmax, y2)
for col in range(xmin, xmax):
row = y2
self.H_routing_cong[row * self.grid_col + col] += weight
# V routing (x1, y1) to (x1, y2)
for row in range(min(y1, y2), max(y1, y2)):
col = x1
self.V_routing_cong[row * self.grid_col + col] += weight
# V routing (x3, y3) to (x3, y2)
for row in range(min(y2, y3), max(y2, y3)):
col = x3
self.V_routing_cong[row * self.grid_col + col] += weight
pass
def __three_pin_net_routing(self, node_gcells, weight):
temp_gcell = list(node_gcells)
## Sorted based on X
temp_gcell.sort(key = lambda x: (x[1], x[0]))
y1, x1 = temp_gcell[0]
y2, x2 = temp_gcell[1]
y3, x3 = temp_gcell[2]
if x1 < x2 and x2 < x3 and min(y1, y3) < y2 and max(y1, y3) > y2:
# print('sk1')
self.l_routing(temp_gcell, weight)
return
if x2 == x3 and x1 < x2 and y1 < min(y2, y3):
# print('sk2')
for col_idx in range(x1,x2,1):
row = y1
col = col_idx
self.H_routing_cong[row * self.grid_col + col] += weight
for row_idx in range(y1, max(y2,y3)):
col = x2
row = row_idx
self.V_routing_cong[row * self.grid_col + col] += weight
return
if y2 == y3:
# print('sk3')
for col in range(x1, x2):
row = y1
self.H_routing_cong[row * self.grid_col + col] += weight
for col in range(x2, x3):
row = y2
self.H_routing_cong[row * self.grid_col + col] += weight
for row in range(min(y2, y1), max(y2, y1)):
col = x2
self.V_routing_cong[row * self.grid_col + col] += weight
return
# print('sk4')
self.t_routing(temp_gcell, weight)
return
def __macro_route_over_grid_cell(self, mod_x, mod_y, mod_w, mod_h):
"""
private function for add module to grid cells
"""
# Two corners
ur = (mod_x + (mod_w/2), mod_y + (mod_h/2))
bl = (mod_x - (mod_w/2), mod_y - (mod_h/2))
# construct block based on current module
module_block = Block(
x_max=mod_x + (mod_w/2),
y_max=mod_y + (mod_h/2),
x_min=mod_x - (mod_w/2),
y_min=mod_y - (mod_h/2)
)
# Only need two corners of a grid cell
ur_row, ur_col = self.__get_grid_cell_location(*ur)
bl_row, bl_col = self.__get_grid_cell_location(*bl)
# check if out of bound
if ur_row >= 0 and ur_col >= 0:
if bl_row < 0:
bl_row = 0
if bl_col < 0:
bl_col = 0
else:
# OOB, skip module
return
if bl_row >= 0 and bl_col >= 0:
if ur_row > self.grid_row - 1:
ur_row = self.grid_row - 1
if ur_col > self.grid_col - 1:
ur_col = self.grid_col - 1
else:
# OOB, skip module
return
if_PARTIAL_OVERLAP_VERTICAL = False
if_PARTIAL_OVERLAP_HORIZONTAL = False
for r_i in range(bl_row, ur_row + 1):
for c_i in range(bl_col, ur_col + 1):
# construct block based on current cell row/col
grid_cell_block = Block(
x_max= (c_i + 1) * self.grid_width,
y_max= (r_i + 1) * self.grid_height,
x_min= c_i * self.grid_width,
y_min= r_i * self.grid_height
)
x_dist, y_dist = self.__overlap_dist(module_block, grid_cell_block)
if ur_row != bl_row:
if (r_i == bl_row and abs(y_dist - self.grid_height) > 1e-5) or (r_i == ur_row and abs(y_dist - self.grid_height) > 1e-5):
if_PARTIAL_OVERLAP_VERTICAL = True
if ur_col != bl_col:
if (c_i == bl_col and abs(x_dist - self.grid_width) > 1e-5) or (c_i == ur_col and abs(x_dist - self.grid_width) > 1e-5):
if_PARTIAL_OVERLAP_HORIZONTAL = True
self.V_macro_routing_cong[r_i * self.grid_col + c_i] += x_dist * self.vrouting_alloc
self.H_macro_routing_cong[r_i * self.grid_col + c_i] += y_dist * self.hrouting_alloc
if if_PARTIAL_OVERLAP_VERTICAL:
for r_i in range(ur_row, ur_row + 1):
for c_i in range(bl_col, ur_col + 1):
grid_cell_block = Block(
x_max= (c_i + 1) * self.grid_width,
y_max= (r_i + 1) * self.grid_height,
x_min= c_i * self.grid_width,
y_min= r_i * self.grid_height
)
x_dist, y_dist = self.__overlap_dist(module_block, grid_cell_block)
self.V_macro_routing_cong[r_i * self.grid_col + c_i] -= x_dist * self.vrouting_alloc
if if_PARTIAL_OVERLAP_HORIZONTAL:
for r_i in range(bl_row, ur_row + 1):
for c_i in range(ur_col, ur_col + 1):
grid_cell_block = Block(
x_max= (c_i + 1) * self.grid_width,
y_max= (r_i + 1) * self.grid_height,
x_min= c_i * self.grid_width,
y_min= r_i * self.grid_height
)
x_dist, y_dist = self.__overlap_dist(module_block, grid_cell_block)
self.H_macro_routing_cong[r_i * self.grid_col + c_i] -= y_dist * self.hrouting_alloc
def __split_net(self, source_gcell, node_gcells):
splitted_netlist = []
for node_gcell in node_gcells:
if node_gcell != source_gcell:
splitted_netlist.append({source_gcell, node_gcell})
return splitted_netlist
def get_vertical_routing_congestion(self):
# TODO: detect if we need to run
self.get_routing()
return self.V_routing_cong
def get_horizontal_routing_congestion(self):
# TODO: detect if we need to run
self.get_routing()
return self.H_routing_cong
def get_routing(self):
self.grid_width = float(self.width/self.grid_col)
self.grid_height = float(self.height/self.grid_row)
self.grid_v_routes = self.grid_width * self.vroutes_per_micron
self.grid_h_routes = self.grid_height * self.hroutes_per_micron
# reset grid
self.H_routing_cong = [0] * self.grid_row * self.grid_col
self.V_routing_cong = [0] * self.grid_row * self.grid_col
self.H_macro_routing_cong = [0] * self.grid_row * self.grid_col
self.V_macro_routing_cong = [0] * self.grid_row * self.grid_col
net_count = 0
for mod in self.modules_w_pins:
norm_fact = 1.0
curr_type = mod.get_type()
# bounding box data structure
node_gcells = set()
source_gcell = None
weight = 1
# NOTE: connection only defined on PORT, soft/hard macro pins
if curr_type == "PORT" and mod.get_sink():
# add source grid location
source_gcell = self.__get_grid_cell_location(*(mod.get_pos()))
node_gcells.add(self.__get_grid_cell_location(*(mod.get_pos())))
for sink_name in mod.get_sink():
for sink_pin in mod.get_sink()[sink_name]:
# retrieve indx in modules_w_pins
sink_idx = self.mod_name_to_indices[sink_pin]
# retrieve sink object
sink = self.modules_w_pins[sink_idx]
# retrieve grid location
node_gcells.add(self.__get_grid_cell_location(*(sink.get_pos())))
elif (curr_type == "macro_pin" or curr_type == "MACRO_PIN") and mod.get_sink():
# add source position
node_gcells.add(self.__get_grid_cell_location(*(mod.get_pos())))
source_gcell = self.__get_grid_cell_location(*(mod.get_pos()))
if mod.get_weight() > 1:
weight = mod.get_weight()
for input_list in mod.get_sink().values():
for sink_name in input_list:
# retrieve indx in modules_w_pins
sink_idx = self.mod_name_to_indices[sink_name]
# retrieve sink object
sink = self.modules_w_pins[sink_idx]
# retrieve grid location
node_gcells.add(self.__get_grid_cell_location(*(sink.get_pos())))
elif curr_type == "MACRO":
module_h = mod.get_height()
module_w = mod.get_width()
module_x, module_y = mod.get_pos()
# compute overlap
self.__macro_route_over_grid_cell(module_x, module_y, module_w, module_h)
if len(node_gcells) == 2:
self.__two_pin_net_routing(source_gcell=source_gcell,node_gcells=node_gcells, weight=weight)
elif len(node_gcells) == 3:
self.__three_pin_net_routing(node_gcells=node_gcells, weight=weight)
elif len(node_gcells) > 3:
for curr_net in self.__split_net(source_gcell=source_gcell, node_gcells=node_gcells):
self.__two_pin_net_routing(source_gcell=source_gcell, node_gcells=curr_net, weight=weight)
# print("V_routing_cong", self.V_routing_cong)
# print("H_routing_cong", self.H_routing_cong)
# normalize routing congestion
for idx, v_gcell in enumerate(self.V_routing_cong):
self.V_routing_cong[idx] = float(v_gcell / self.grid_v_routes)
for idx, h_gcell in enumerate(self.H_routing_cong):
self.H_routing_cong[idx] = float(h_gcell / self.grid_h_routes)
for idx, v_gcell in enumerate(self.V_macro_routing_cong):
self.V_macro_routing_cong[idx] = float(v_gcell / self.grid_v_routes)
for idx, h_gcell in enumerate(self.H_macro_routing_cong):
self.H_macro_routing_cong[idx] = float(h_gcell / self.grid_h_routes)
self.__smooth_routing_cong()
# sum up routing congestion with macro congestion
self.V_routing_cong = [sum(x) for x in zip(self.V_routing_cong, self.V_macro_routing_cong)]
self.H_routing_cong = [sum(x) for x in zip(self.H_routing_cong, self.H_macro_routing_cong)]
def __smooth_routing_cong(self):
temp_V_routing_cong = [0] * self.grid_col * self.grid_row
temp_H_routing_cong = [0] * self.grid_col * self.grid_row
# v routing cong
for row in range(self.grid_row):
for col in range(self.grid_col):
lp = col - self.smooth_range
if lp < 0:
lp = 0
rp = col + self.smooth_range
if rp >= self.grid_col:
rp = self.grid_col - 1
gcell_cnt = rp - lp + 1
val = self.V_routing_cong[row * self.grid_col + col] / gcell_cnt
for ptr in range(lp, rp + 1, 1):
temp_V_routing_cong[row * self.grid_col + ptr] += val
self.V_routing_cong = temp_V_routing_cong
# h routing cong
for row in range(self.grid_row):
for col in range(self.grid_col):
lp = row - self.smooth_range
if lp < 0:
lp = 0
up = row + self.smooth_range
if up >= self.grid_row:
up = self.grid_row - 1
gcell_cnt = up - lp + 1
val = self.H_routing_cong[row * self.grid_col + col] / gcell_cnt
for ptr in range(lp, up + 1, 1):
temp_H_routing_cong[ptr * self.grid_col + col] += val
self.H_routing_cong = temp_H_routing_cong
def is_node_soft_macro(self, node_idx) -> bool:
return self.get_node_type(node_idx) == "soft_macro"
def is_node_hard_macro(self, node_idx) -> bool:
return self.get_node_type(node_idx) == "hard_macro"
def get_node_name(self, node_idx: int) -> str:
return self.indices_to_mod_name[node_idx]
def get_node_mask(self, node_idx: int, node_name: str) -> list:
return list
"""
Return Grid_col x Grid_row:
1 == placable
0 == unplacable
(100, 100) => 5
(99, 99) => 0
(100, 99) => 1
(99, 100) => 4
Placement Constraint:
- center @ grid cell
- no overlapping other macro
- no OOB
"""
module = self.modules_w_pins[node_idx]
def get_node_type(self, node_idx: int) -> str:
"""
......@@ -542,23 +1201,22 @@ class PlacementCost(object):
def get_macro_adjacency(self) -> list:
"""
Compute Adjacency Matrix (Unclustered PORTs)
Compute Adjacency Matrix
"""
# NOTE: in pb.txt, netlist input count exceed certain threshold will be ommitted
macro_adj = [0] * self.module_cnt * self.module_cnt
assert len(macro_adj) == self.module_cnt * self.module_cnt
#[MACRO][macro]
module_indices = self.hard_macro_indices + self.soft_macro_indices
macro_adj = [0] * (self.hard_macro_cnt + self.soft_macro_cnt) * (self.hard_macro_cnt + self.soft_macro_cnt)
assert len(macro_adj) == (self.hard_macro_cnt + self.soft_macro_cnt) * (self.hard_macro_cnt + self.soft_macro_cnt)
#[MACRO][macro][PORT]
module_indices = self.hard_macro_indices + self.soft_macro_indices + self.port_indices
for row_idx, module_idx in enumerate(module_indices):
for row_idx, module_idx in enumerate(sorted(module_indices)):
# row index
# store temp module
curr_module = self.modules_w_pins[module_idx]
# get module name
curr_module_name = curr_module.get_name()
for col_idx, h_module_idx in enumerate(module_indices):
for col_idx, h_module_idx in enumerate(sorted(module_indices)):
# col index
entry = 0
# store connected module
......@@ -572,19 +1230,14 @@ class PlacementCost(object):
if h_module_name in curr_module.get_connection():
entry += curr_module.get_connection()[h_module_name]
macro_adj[row_idx * self.module_cnt + col_idx] = entry
macro_adj[row_idx * (self.hard_macro_cnt + self.soft_macro_cnt) + col_idx] = entry
macro_adj[col_idx * (self.hard_macro_cnt + self.soft_macro_cnt) + row_idx] = entry
return macro_adj
def is_node_fixed(self):
pass
def unplace_all_nodes(self):
pass
def place_node(self):
pass
def restore_placement(self, init_plc_pth: str):
"""
Read and retrieve .plc file information
......@@ -610,22 +1263,49 @@ class PlacementCost(object):
def get_macro_and_clustered_port_adjacency(self):
"""
Compute Adjacency Matrix (Unclustered PORTs)
if module is a PORT, assign nearest cell location even if OOB
"""
#[MACRO][macro]
module_indices = self.hard_macro_indices + self.soft_macro_indices
#[Grid Cell] => [PORT]
clustered_ports = {}
for port_idx in self.port_indices:
port = self.modules_w_pins[port_idx]
x_pos, y_pos = port.get_pos()
row, col = self.__get_grid_cell_location(x_pos=x_pos, y_pos=y_pos)
# prevent OOB
if row >= self.grid_row:
row = self.grid_row - 1
if row < 0:
row = 0
if col >= self.grid_col:
col = self.grid_col - 1
if col < 0:
col = 0
if (row, col) in clustered_ports:
clustered_ports[(row, col)].append(port)
else:
clustered_ports[(row, col)] = [port]
# NOTE: in pb.txt, netlist input count exceed certain threshold will be ommitted
macro_adj = [0] * self.module_cnt * self.module_cnt
assert len(macro_adj) == self.module_cnt * self.module_cnt
#[MACRO][macro][PORT]
module_indices = self.hard_macro_indices + self.soft_macro_indices + self.port_indices
macro_adj = [0] * (len(module_indices) + len(clustered_ports)) * (len(module_indices) + len(clustered_ports))
cell_location = [0] * len(clustered_ports)
for row_idx, module_idx in enumerate(module_indices):
# row index
# instantiate macros
for row_idx, module_idx in enumerate(sorted(module_indices)):
# store temp module
curr_module = self.modules_w_pins[module_idx]
# get module name
curr_module_name = curr_module.get_name()
for col_idx, h_module_idx in enumerate(module_indices):
for col_idx, h_module_idx in enumerate(sorted(module_indices)):
# col index
entry = 0
# store connected module
......@@ -639,43 +1319,161 @@ class PlacementCost(object):
if h_module_name in curr_module.get_connection():
entry += curr_module.get_connection()[h_module_name]
macro_adj[row_idx * self.module_cnt + col_idx] = entry
macro_adj[row_idx * (len(module_indices) + len(clustered_ports)) + col_idx] = entry
macro_adj[col_idx * (len(module_indices) + len(clustered_ports)) + row_idx] = entry
# instantiate clustered ports
for row_idx, cluster_cell in enumerate(sorted(clustered_ports, key=lambda tup: tup[1])):
# print("cluster_cell", cluster_cell)
# print("port cnt", len(clustered_ports[cluster_cell]))
# add cell location
cell_location[row_idx] = cluster_cell[0] * self.grid_col + cluster_cell[1]
# relocate to after macros
row_idx += len(module_indices)
# for each port within a grid cell
for curr_port in clustered_ports[cluster_cell]:
# get module name
curr_port_name = curr_port.get_name()
# print("curr_port_name", curr_port_name, curr_port.get_pos())
# assuming ports only connects to macros
for col_idx, h_module_idx in enumerate(module_indices):
# col index
entry = 0
# store connected module
h_module = self.modules_w_pins[h_module_idx]
# get connected module name
h_module_name = h_module.get_name()
# print("other connections", h_module.get_connection(), curr_port_name)
if curr_port_name in h_module.get_connection():
entry += h_module.get_connection()[curr_port_name]
if h_module_name in curr_port.get_connection():
entry += curr_port.get_connection()[h_module_name]
macro_adj[row_idx * (len(module_indices) + len(clustered_ports)) + col_idx] += entry
macro_adj[col_idx * (len(module_indices) + len(clustered_ports)) + row_idx] += entry
return macro_adj, sorted(cell_location)
def get_node_location(self, node_idx):
pass
return macro_adj
def get_grid_cell_of_node(self, node_idx):
""" if grid_cell at grid crossing, break-tie to upper right
"""
return self.modules_w_pins(node_idx).get_location()
def update_macro_orientation(self, node_idx, orientation):
pass
def get_node_location(self):
def get_macro_orientation(self):
pass
def get_grid_cell_of_node(self):
def unfix_node_coord(self):
"""In case plc is loaded with fixed macros
"""
pass
def update_macro_orientation(self):
def fix_node_coord(self):
"""Find all ports and fix their coordinates.
"""
pass
def get_macro_orientation(self):
def unplace_all_nodes(self):
pass
def unfix_node_coord(self):
def place_node(self, node_idx, grid_cell_idx):
pass
def unplace_node(self):
def can_place_node(self, node_idx, grid_cell_idx):
return self.get_node_mask(node_idx=node_idx)[grid_cell_idx]
def unplace_node(self, node_idx):
# update node_mask
pass
def is_node_placed(self):
def is_node_placed(self, node_idx):
pass
def get_source_filename(self):
def disconnect_nets(self):
pass
def get_source_filename(self):
"""return netlist path
"""
return self.netlist_file
def get_blockages(self):
pass
def get_ref_node_id(self):
pass
def get_ref_node_id(self, node_idx=-1):
"""ref_node_id is used for macro_pins. Refers to the macro it belongs to.
"""
if node_idx != -1:
if node_idx in self.soft_macro_pin_indices:
pin = self.modules_w_pins[node_idx]
return self.mod_name_to_indices[pin.get_macro_name()]
elif node_idx in self.hard_macro_pin_indices:
pin = self.modules_w_pins[node_idx]
return self.mod_name_to_indices[pin.get_macro_name()]
return -1
def save_placement(self):
pass
def display_canvas( self,
annotate=True,
amplify=False):
#define Matplotlib figure and axis
fig, ax = plt.subplots(figsize=(8,8), dpi=50)
if amplify:
PORT_SIZE = 4
FONT_SIZE = 10
PIN_SIZE = 4
else:
PORT_SIZE = 2
FONT_SIZE = 5
PIN_SIZE = 2
# Plt config
ax.margins(x=0.05, y=0.05)
ax.set_aspect('equal', adjustable='box')
# Construct grid
x, y = np.meshgrid(np.linspace(0, self.width, self.grid_col + 1),\
np.linspace(0, self.height, self.grid_row + 1))
ax.plot(x, y, c='b', alpha=0.1) # use plot, not scatter
ax.plot(np.transpose(x), np.transpose(y), c='b', alpha=0.2) # add this here
# Construct module blocks
for mod in self.modules_w_pins:
if mod.get_type() == 'PORT':
plt.plot(*mod.get_pos(),'ro', markersize=PORT_SIZE)
elif mod.get_type() == 'MACRO':
ax.add_patch(Rectangle((mod.get_pos()[0] - mod.get_width()/2, mod.get_pos()[1] - mod.get_height()/2),\
mod.get_width(), mod.get_height(),\
alpha=0.5, zorder=1000, facecolor='b', edgecolor='darkblue'))
if annotate:
ax.annotate(mod.get_name(), mod.get_pos(), color='r', weight='bold', fontsize=FONT_SIZE, ha='center', va='center')
elif mod.get_type() == 'MACRO_PIN':
plt.plot(*mod.get_pos(),'bo', markersize=PIN_SIZE)
elif mod.get_type() == 'macro':
ax.add_patch(Rectangle((mod.get_pos()[0] - mod.get_width()/2, mod.get_pos()[1] - mod.get_height()/2),\
mod.get_width(), mod.get_height(),\
alpha=0.5, zorder=1000, facecolor='y'))
if annotate:
ax.annotate(mod.get_name(), mod.get_pos(), wrap=True,color='r', weight='bold', fontsize=FONT_SIZE, ha='center', va='center')
plt.show()
plt.close('all')
# Board Entity Definition
class Port:
def __init__(self, name, x = 0.0, y = 0.0, side = "BOTTOM"):
......@@ -685,6 +1483,8 @@ class PlacementCost(object):
self.side = side # "BOTTOM", "TOP", "LEFT", "RIGHT"
self.sink = {} # standard cells, macro pins, ports driven by this cell
self.connection = {} # [module_name] => edge degree
self.ifFixed = True
self.placement = 0 # needs to be updated
def get_name(self):
return self.name
......@@ -759,13 +1559,15 @@ class PlacementCost(object):
self.height = float(height)
self.x = float(x)
self.y = float(y)
self.sink = {} # [MACRO_NAME] => [PIN_NAME]
self.connection = {} # [module_name] => edge degree
self.ifFixed = False
self.ifPlaced = True
self.location = 0 # needs to be updated
def get_name(self):
return self.name
def add_connection(self, module_name):
def add_connection(self, module_name, weight):
# NOTE: assume PORT names does not contain slash
ifPORT = False
module_name_splited = module_name.rsplit('/', 1)
......@@ -774,18 +1576,19 @@ class PlacementCost(object):
if ifPORT:
# adding PORT
self.connection[module_name] = 1
self.connection[module_name] = 1 * weight
else:
# adding soft/hard macros
if module_name_splited[0] in self.connection.keys():
self.connection[module_name_splited[0]] += 1
self.connection[module_name_splited[0]] += 1 * weight
else:
self.connection[module_name_splited[0]] = 1
self.connection[module_name_splited[0]] = 1 * weight
def add_connections(self, module_names):
def add_connections(self, module_names, weight):
# NOTE: assume PORT names does not contain slash
# consider weight on soft macro pins
for module_name in module_names:
self.add_connection(module_name)
self.add_connection(module_name, weight)
def set_pos(self, x, y):
self.x = x
......@@ -794,9 +1597,6 @@ class PlacementCost(object):
def get_pos(self):
return self.x, self.y
def get_sink(self):
return self.sink
def get_type(self):
return "macro"
......@@ -806,9 +1606,22 @@ class PlacementCost(object):
def get_area(self):
return self.width * self.height
def get_height(self):
return self.height
def get_width(self):
return self.width
def set_location(self, grid_cell_idx):
self.location = grid_cell_idx
def get_location(self):
return self.location
class SoftMacroPin:
def __init__(self, name,
x = 0.0, y = 0.0, macro_name = "", weight = 0.0):
def __init__( self, name,
x = 0.0, y = 0.0,
macro_name = "", weight = 1.0):
self.name = name
self.x = float(x)
self.y = float(y)
......@@ -827,6 +1640,9 @@ class PlacementCost(object):
def get_name(self):
return self.name
def get_macro_name(self):
return self.macro_name
def set_pos(self, x, y):
self.x = x
self.y = y
......@@ -861,6 +1677,9 @@ class PlacementCost(object):
def get_sink(self):
return self.sink
def get_weight(self):
return self.weight
def get_type(self):
return "macro_pin"
......@@ -875,11 +1694,14 @@ class PlacementCost(object):
self.y = float(y)
self.orientation = orientation
self.connection = {} # [module_name] => edge degree
self.ifFixed = False
self.ifPlaced = True
self.location = 0 # needs to be updated
def get_name(self):
return self.name
def add_connection(self, module_name):
def add_connection(self, module_name, weight):
# NOTE: assume PORT names does not contain slash
ifPORT = False
module_name_splited = module_name.rsplit('/', 1)
......@@ -888,18 +1710,19 @@ class PlacementCost(object):
if ifPORT:
# adding PORT
self.connection[module_name] = 1
self.connection[module_name] = 1 * weight
else:
# adding soft/hard macros
if module_name_splited[0] in self.connection.keys():
self.connection[module_name_splited[0]] += 1
self.connection[module_name_splited[0]] += 1 * weight
else:
self.connection[module_name_splited[0]] = 1
self.connection[module_name_splited[0]] = 1 * weight
def add_connections(self, module_names):
def add_connections(self, module_names, weight):
# NOTE: assume PORT names does not contain slash
# consider weight on soft macro pins
for module_name in module_names:
self.add_connection(module_name)
self.add_connection(module_name, weight)
def get_connection(self):
return self.connection
......@@ -920,11 +1743,23 @@ class PlacementCost(object):
def get_area(self):
return self.width * self.height
def get_height(self):
return self.height
def get_width(self):
return self.width
def set_location(self, grid_cell_idx):
self.location = grid_cell_idx
def get_location(self):
return self.location
class HardMacroPin:
def __init__(self, name,
x = 0.0, y = 0.0,
x_offset = 0.0, y_offset = 0.0,
macro_name = "", weight = 0.0):
macro_name = "", weight = 1.0):
self.name = name
self.x = float(x)
self.y = float(y)
......@@ -933,6 +1768,7 @@ class PlacementCost(object):
self.macro_name = macro_name
self.weight = weight
self.sink = {}
self.ifPlaced = True
def set_weight(self, weight):
self.weight = weight
......@@ -953,6 +1789,9 @@ class PlacementCost(object):
def get_name(self):
return self.name
def get_macro_name(self):
return self.macro_name
def add_sink(self, sink_name):
# NOTE: assume PORT names does not contain slash
ifPORT = False
......@@ -980,3 +1819,38 @@ class PlacementCost(object):
def get_type(self):
return "MACRO_PIN"
# TODO finish this
# class StandardCell:
# def __init__( self, name,
# x = 0.0, y = 0.0, weight = 1.0):
# self.name = name
# self.x = float(x)
# self.y = float(y)
# self.x_offset = 0.0 # not used
# self.y_offset = 0.0 # not used
# self.macro_name = macro_name
# self.weight = weight
# self.sink = {}
def main():
test_netlist_dir = './Plc_client/test/'+\
'ariane133'
netlist_file = os.path.join(test_netlist_dir,
'netlist.pb.txt')
plc = PlacementCost(netlist_file)
print(plc.get_block_name())
print("Area: ", plc.get_area())
print("Wirelength: ", plc.get_wirelength())
print("# HARD_MACROs : %d"%(plc.get_hard_macro_count()))
print("# HARD_MACRO_PINs : %d"%(plc.get_hard_macro_pin_count()))
print("# MACROs : %d"%(plc.get_hard_macro_count() + plc.get_soft_macro_count()))
print("# MACRO_PINs : %d"%(plc.get_hard_macro_pin_count() + plc.get_soft_macro_pin_count()))
print("# PORTs : %d"%(plc.get_port_count()))
print("# SOFT_MACROs : %d"%(plc.get_soft_macro_count()))
print("# SOFT_MACRO_PINs : %d"%(plc.get_soft_macro_pin_count()))
print("# STDCELLs : 0")
if __name__ == '__main__':
main()
\ No newline at end of file
CodeElements/Plc_client/plc_client_os_test.py
import unittest
import os
from absl import flags
from absl import app
from torch import feature_alpha_dropout
from Plc_client import plc_client_os as plc_client_os
from Plc_client import plc_client as plc_client
import numpy as np
import sys
import time
import math
np.set_printoptions(threshold=sys.maxsize)
FLAGS = flags.FLAGS
class CircuitDataBaseTest(unittest.TestCase):
class CircuitDataBaseTest():
# NETLIST_PATH = "./Plc_client/test/sample_clustered_uniform_two_soft/netlist.pb.txt"
# NETLIST_PATH = "./Plc_client/test/ariane_hard2soft/netlist.pb.txt"
# NETLIST_PATH = "./Plc_client/test/ariane_soft2hard/netlist.pb.txt"
# NETLIST_PATH = "./Plc_client/test/ariane_port2soft/netlist.pb.txt"
# NETLIST_PATH = "./Plc_client/test/sample_clustered_nomacro/netlist.pb.txt"
# NETLIST_PATH = "./Plc_client/test/sample_clustered_macroxy/netlist.pb.txt"
# NETLIST_PATH = "./Plc_client/test/ariane/netlist.pb.txt"
# NETLIST_PATH = "./Plc_client/test/ariane133/netlist.pb.txt"
NETLIST_PATH = "./Plc_client/test/testcases/TC1_MP1_0_0_P2_0_1.pb.txt"
# NETLIST_PATH = "./Plc_client/test/testcases/TC24_MP1_0_0_MP2_4_4.pb.txt"
# NETLIST_PATH = "./Plc_client/test/0P1M1m/netlist.pb.txt"
# NETLIST_PATH = "./Plc_client/test/0P2M0m/netlist.pb.txt"
# NETLIST_PATH = "./Plc_client/test/0P3M0m/netlist.pb.txt"
# NETLIST_PATH = "./Plc_client/test/0P4M0m/netlist.pb.txt"
# NETLIST_PATH = "./Plc_client/test/testcases_xm/TC_MP1_4_1_MP2_2_2_MP3_3_4_MP4_0_0.pb.txt"
# Google's Ariane
# CANVAS_WIDTH = 356.592
# CANVAS_HEIGHT = 356.640
# GRID_COL = 35
# GRID_ROW = 33
# Ariane133
CANVAS_WIDTH = 1599.99
CANVAS_HEIGHT = 1600.06
GRID_COL = 24
GRID_ROW = 21
# Sample clustered
# CANVAS_WIDTH = 400
# CANVAS_HEIGHT = 400
# GRID_COL = 4
# GRID_ROW = 4
# PMm
# CANVAS_WIDTH = 100
# CANVAS_HEIGHT = 100
# GRID_COL = 5
# GRID_ROW = 5
def __init__(self, NETLIST_PATH) -> None:
self.NETLIST_PATH = NETLIST_PATH
def test_proxy_congestion(self):
# Google's Binary Executable
self.plc = plc_client.PlacementCost(self.NETLIST_PATH)
self.plc_os = plc_client_os.PlacementCost(self.NETLIST_PATH)
# set rpm
self.plc.set_routes_per_micron(10, 10)
self.plc_os.set_routes_per_micron(10, 10)
# self.plc.set_macro_routing_allocation(5, 5)
# self.plc_os.set_macro_routing_allocation(5, 5)
self.plc.set_macro_routing_allocation(0, 0)
self.plc_os.set_macro_routing_allocation(0, 0)
self.plc.set_congestion_smooth_range(0.0)
self.plc_os.set_congestion_smooth_range(0.0)
self.plc.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc.set_placement_grid(self.GRID_COL, self.GRID_ROW)
self.plc_os.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc_os.set_placement_grid(self.GRID_COL, self.GRID_ROW)
print("Name: ", self.plc.get_source_filename().rsplit("/", 1)[1])
# self.plc_os.display_canvas(amplify=True)
# start = time.time()
temp_gl_h = np.array(self.plc.get_horizontal_routing_congestion())
temp_os_h = np.array(self.plc_os.get_horizontal_routing_congestion())
print(temp_gl_h.reshape(self.GRID_COL, self.GRID_ROW))
print(temp_os_h.reshape(self.GRID_COL, self.GRID_ROW))
print("GL H Congestion: ", temp_gl_h)
print("OS H Congestion: ", temp_os_h)
temp_gl_v = np.array(self.plc.get_vertical_routing_congestion())
temp_os_v = np.array(self.plc_os.get_vertical_routing_congestion())
print(temp_gl_v.reshape(self.GRID_COL, self.GRID_ROW))
print(temp_os_v.reshape(self.GRID_COL, self.GRID_ROW))
print("GL V Congestion: ", self.plc.get_vertical_routing_congestion())
print("OS V Congestion: ", self.plc_os.get_vertical_routing_congestion())
print("congestion GL: ", self.plc.get_congestion_cost())
print("congestion OS: ", self.plc_os.get_congestion_cost())
if abs(self.plc.get_congestion_cost() - self.plc_os.get_congestion_cost()) > 1e-3:
print("MISMATCH FOUND!!!****************")
else:
print("MATCHED!!! **********************")
###################################################################### EXTRACT ROUTING CONGESTION
self.plc.set_macro_routing_allocation(0, 0)
self.plc_os.set_macro_routing_allocation(0, 0)
temp_gl_h_rt = np.array(self.plc.get_horizontal_routing_congestion())
temp_os_h_rt = np.array(self.plc_os.get_horizontal_routing_congestion())
temp_gl_v_rt = np.array(self.plc.get_vertical_routing_congestion())
temp_os_v_rt = np.array(self.plc_os.get_vertical_routing_congestion())
temp_gl_h_mc = (temp_gl_h - temp_gl_h_rt).reshape(self.GRID_COL, self.GRID_ROW)
temp_os_h_mc = (temp_os_h - temp_os_h_rt).reshape(self.GRID_COL, self.GRID_ROW)
temp_gl_v_mc = (temp_gl_v - temp_gl_v_rt).reshape(self.GRID_COL, self.GRID_ROW)
temp_os_v_mc = (temp_os_v - temp_os_v_rt).reshape(self.GRID_COL, self.GRID_ROW)
# print("GL H MACRO Congestion", (temp_gl_h_mc).reshape(self.GRID_COL, self.GRID_ROW))
# print("OS H MACRO Congestion", (temp_os_h_mc).reshape(self.GRID_COL, self.GRID_ROW))
print("H MACRO Congestion DIFF", np.where(abs(temp_gl_h_mc - temp_os_h_mc) > 1e-5))
# print("GL V MACRO Congestion", (temp_gl_v_mc).reshape(self.GRID_COL, self.GRID_ROW))
# print("OS V MACRO Congestion", (temp_os_v_mc).reshape(self.GRID_COL, self.GRID_ROW))
print("V MACRO Congestion DIFF", np.where(abs(temp_gl_v_mc - temp_os_v_mc) > 1e-5))
####################################################################### BY ENTRY
print("**************BY ENTRY DIFF")
print(temp_gl_h_mc[0][6], temp_os_h_mc[0][6])
# print(temp_gl_v_mc[1][6], temp_os_v_mc[1][6])
######################################################################
# end = time.time()
# print("time elapsed:", end - start)
# for idx in range(len(temp_gl_h)):
# print("gl, os:", temp_gl_h[idx], temp_os_h[idx], temp_gl_v[idx], temp_os_v[idx])
# print("congestion summation gl os", sum(temp_gl_h), sum(temp_os_h), sum(temp_gl_v), sum(temp_os_v))
def test_proxy_cost(self):
# Google's Binary Executable
self.plc = plc_client_os.PlacementCost(self.NETLIST_PATH)
self.plc = plc_client.PlacementCost(self.NETLIST_PATH)
# Open-sourced Implementation
self.plc_os = plc_client_os.PlacementCost(netlist_file=self.NETLIST_PATH,
macro_macro_x_spacing = 50,
macro_macro_y_spacing = 50)
self.plc.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc.set_placement_grid(self.GRID_COL, self.GRID_ROW)
self.plc_os.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc_os.set_placement_grid(self.GRID_COL, self.GRID_ROW)
# print(self.plc_os.display_canvas())
print(self.plc_os.get_wirelength(), self.plc.get_wirelength())
assert int(self.plc_os.get_wirelength()) == int(self.plc.get_wirelength())
print("os wl cost", self.plc_os.get_cost())
print("gl wl cost", self.plc.get_cost())
assert abs(self.plc.get_cost() - self.plc_os.get_cost()) <= 10e-3
print("gl density\n", np.array(self.plc.get_grid_cells_density()).reshape(self.GRID_COL, self.GRID_ROW))
print("os density\n", np.array(self.plc_os.get_grid_cells_density()).reshape(self.GRID_COL, self.GRID_ROW))
assert int(sum(self.plc_os.get_grid_cells_density())) == int(sum(self.plc.get_grid_cells_density()))
assert int(self.plc_os.get_density_cost()) == int(self.plc.get_density_cost())
print("os density cost", self.plc_os.get_density_cost())
print("gl density cost", self.plc.get_density_cost())
def test_metadata(self):
# Google's Binary Executable
self.plc = plc_client_os.PlacementCost(self.NETLIST_PATH)
self.plc = plc_client.PlacementCost(self.NETLIST_PATH)
# Open-sourced Implementation
self.plc_os = plc_client_os.PlacementCost(netlist_file=self.NETLIST_PATH,
macro_macro_x_spacing = 50,
macro_macro_y_spacing = 50)
self.plc.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc.set_placement_grid(self.GRID_COL, self.GRID_ROW)
self.plc_os.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc_os.set_placement_grid(self.GRID_COL, self.GRID_ROW)
self.plc_os.get_grid_cells_density()
assert self.plc_os.get_block_name() == self.plc_os.get_block_name()
assert self.plc_os.get_project_name() == self.plc_os.get_project_name()
assert int(self.plc_os.get_area()) == int(self.plc_os.get_area())
assert int(self.plc_os.get_area()) == int(self.plc.get_area())
self.plc.set_routes_per_micron(1.0, 2.0)
self.plc_os.set_routes_per_micron(1.0, 2.0)
......@@ -50,16 +208,65 @@ class CircuitDataBaseTest(unittest.TestCase):
self.plc_os.set_macro_routing_allocation(3.0, 4.0)
assert self.plc.get_macro_routing_allocation() == self.plc_os.get_macro_routing_allocation()
assert self.plc.get_macro_adjacency() == self.plc_os.get_macro_adjacency()
# test get_macro_adjacency
plc_macroadj = self.plc.get_macro_adjacency()
plc_macroadj = np.array(plc_macroadj).reshape(int(math.sqrt(len(plc_macroadj))),\
int(math.sqrt(len(plc_macroadj))))
plcos_macroadj = self.plc_os.get_macro_adjacency()
plcos_macroadj = np.array(plcos_macroadj).reshape(int(math.sqrt(len(plcos_macroadj))),\
int(math.sqrt(len(plcos_macroadj))))
assert(np.sum(np.nonzero(plc_macroadj - plcos_macroadj)) == 0)
# test get_macro_and_clustered_port_adjacency
plc_clusteradj, plc_cell = self.plc.get_macro_and_clustered_port_adjacency()
plc_clusteradj = np.array(plc_clusteradj).reshape(int(math.sqrt(len(plc_clusteradj))),\
int(math.sqrt(len(plc_clusteradj))))
plcos_clusteradj, plcos_cell = self.plc_os.get_macro_and_clustered_port_adjacency()
plcos_clusteradj = np.array(plcos_clusteradj).reshape(int(math.sqrt(len(plcos_clusteradj))),\
int(math.sqrt(len(plcos_clusteradj))))
assert(plc_cell == plcos_cell)
for plc_adj, plcos_adj in zip(plc_clusteradj, plcos_clusteradj):
assert(np.sum(np.nonzero(plc_adj - plcos_adj)) == 0)
def test_miscellaneous(self):
# Google's Binary Executable
self.plc = plc_client_os.PlacementCost(self.NETLIST_PATH)
# Open-sourced Implementation
self.plc = plc_client.PlacementCost(self.NETLIST_PATH)
self.plc_os = plc_client_os.PlacementCost(netlist_file=self.NETLIST_PATH,
macro_macro_x_spacing = 50,
macro_macro_y_spacing = 50)
print("****************** miscellaneous ******************")
self.plc.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc.set_placement_grid(self.GRID_COL, self.GRID_ROW)
self.plc_os.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc_os.set_placement_grid(self.GRID_COL, self.GRID_ROW)
NODE_IDX = 0
print("get_macro_indices", self.plc.get_macro_indices(), self.plc_os.get_macro_indices())
print("get_node_name", self.plc.get_node_name(NODE_IDX))
print("get_node_location", self.plc.get_node_location(NODE_IDX))
print("get_grid_cell_of_node", self.plc.get_grid_cell_of_node(NODE_IDX))
print("get_node_location", self.plc.get_node_location(NODE_IDX))
print("get_macro_orientation", self.plc.get_macro_orientation(NODE_IDX))
print("is_node_placed", self.plc.is_node_placed(NODE_IDX))
print("get_source_filename", self.plc.get_source_filename())
print("get_blockages", self.plc.get_blockages())
print("get_ref_node_id", self.plc.get_ref_node_id(NODE_IDX), self.plc.get_ref_node_id(NODE_IDX))
print("get_node_mask\n", np.array(self.plc.get_node_mask(NODE_IDX)).reshape((4,4)))
print("can_place_node", self.plc.can_place_node(0, 1))
print("***************************************************")
def main(argv):
args = sys.argv[1:]
temp = CircuitDataBaseTest(args[0])
temp.test_proxy_congestion()
# temp.test_proxy_cost()
# temp.test_metadata()
# temp.test_miscellaneous()
if __name__ == '__main__':
unittest.main()
\ No newline at end of file
if __name__ == "__main__":
app.run(main)
\ No newline at end of file
CodeElements/Plc_client/test/0P1M1m/netlist.pb.txt 0 → 100644
# proto-file: tensorflow/core/framework/graph.proto
# proto-message: tensorflow.GraphDef
node {
name: "M0"
attr {
key: "height"
value {
f: 50
}
}
attr {
key: "orientation"
value {
placeholder: "N"
}
}
attr {
key: "type"
value {
placeholder: "MACRO"
}
}
attr {
key: "x"
value {
f: 350
}
}
attr {
key: "y"
value {
f: 350
}
}
attr {
key: "width"
value {
f: 50
}
}
}
node {
name: "M0/P1"
input: "Grp_2/Pinput"
attr {
key: "macro_name"
value {
placeholder: "M0"
}
}
attr {
key: "type"
value {
placeholder: "MACRO_PIN"
}
}
attr {
key: "x_offset"
value {
f: 25
}
}
attr {
key: "y_offset"
value {
f: -25
}
}
attr {
key: "x"
value {
f: 375
}
}
attr {
key: "y"
value {
f: 325
}
}
}
node {
name: "Grp_2"
attr {
key: "height"
value {
f: 50
}
}
attr {
key: "type"
value {
placeholder: "macro"
}
}
attr {
key: "width"
value {
f: 50
}
}
attr {
key: "x"
value {
f: 150
}
}
attr {
key: "y"
value {
f: 50
}
}
}
node {
name: "Grp_2/Pinput"
attr {
key: "macro_name"
value {
placeholder: "Grp_2"
}
}
attr {
key: "type"
value {
placeholder: "macro_pin"
}
}
attr {
key: "x"
value {
f: 150
}
}
attr {
key: "x_offset"
value {
f: 0
}
}
attr {
key: "y"
value {
f: 50
}
}
attr {
key: "y_offset"
value {
f: 0
}
}
}
\ No newline at end of file
CodeElements/Plc_client/test/0P2M0m/netlist.pb.txt 0 → 100644
# proto-file: tensorflow/core/framework/graph.proto
# proto-message: tensorflow.GraphDef
node {
name: "M0"
attr {
key: "height"
value {
f: 200
}
}
attr {
key: "orientation"
value {
placeholder: "N"
}
}
attr {
key: "type"
value {
placeholder: "MACRO"
}
}
attr {
key: "x"
value {
f: 399
}
}
attr {
key: "y"
value {
f: 399
}
}
attr {
key: "width"
value {
f: 200
}
}
}
node {
name: "M1"
attr {
key: "height"
value {
f: 50
}
}
attr {
key: "orientation"
value {
placeholder: "N"
}
}
attr {
key: "type"
value {
placeholder: "MACRO"
}
}
attr {
key: "x"
value {
f: 50
}
}
attr {
key: "y"
value {
f: 50
}
}
attr {
key: "width"
value {
f: 50
}
}
}
node {
name: "M0/P1"
input: "M1/P1"
attr {
key: "macro_name"
value {
placeholder: "M0"
}
}
attr {
key: "type"
value {
placeholder: "MACRO_PIN"
}
}
attr {
key: "x_offset"
value {
f: -25
}
}
attr {
key: "y_offset"
value {
f: -25
}
}
attr {
key: "x"
value {
f: 374
}
}
attr {
key: "y"
value {
f: 374
}
}
attr {
key: "weight"
value {
f: 1000
}
}
}
node {
name: "M0/P2"
attr {
key: "macro_name"
value {
placeholder: "M0"
}
}
attr {
key: "type"
value {
placeholder: "MACRO_PIN"
}
}
attr {
key: "x_offset"
value {
f: -25
}
}
attr {
key: "y_offset"
value {
f: 25
}
}
attr {
key: "x"
value {
f: 374
}
}
attr {
key: "y"
value {
f: 424
}
}
attr {
key: "weight"
value {
f: 1000
}
}
}
node {
name: "M1/P1"
attr {
key: "macro_name"
value {
placeholder: "M1"
}
}
attr {
key: "type"
value {
placeholder: "MACRO_PIN"
}
}
attr {
key: "x_offset"
value {
f: 25
}
}
attr {
key: "y_offset"
value {
f: -25
}
}
attr {
key: "x"
value {
f: 75
}
}
attr {
key: "y"
value {
f: 25
}
}
}
node {
name: "M1/P2"
attr {
key: "macro_name"
value {
placeholder: "M1"
}
}
attr {
key: "type"
value {
placeholder: "MACRO_PIN"
}
}
attr {
key: "x_offset"
value {
f: 25
}
}
attr {
key: "y_offset"
value {
f: 25
}
}
attr {
key: "x"
value {
f: 75
}
}
attr {
key: "y"
value {
f: 75
}
}
}
\ No newline at end of file
CodeElements/Plc_client/test/0P3M0m/netlist.pb.txt 0 → 100644
# proto-file: tensorflow/core/framework/graph.proto
# proto-message: tensorflow.GraphDef
node {
name: "M0"
attr {
key: "height"
value {
f: 50
}
}
attr {
key: "orientation"
value {
placeholder: "N"
}
}
attr {
key: "type"
value {
placeholder: "MACRO"
}
}
attr {
key: "x"
value {
f: 350
}
}
attr {
key: "y"
value {
f: 350
}
}
attr {
key: "width"
value {
f: 50
}
}
}
node {
name: "M1"
attr {
key: "height"
value {
f: 50
}
}
attr {
key: "orientation"
value {
placeholder: "N"
}
}
attr {
key: "type"
value {
placeholder: "MACRO"
}
}
attr {
key: "x"
value {
f: 50
}
}
attr {
key: "y"
value {
f: 50
}
}
attr {
key: "width"
value {
f: 50
}
}
}
node {
name: "M2"
attr {
key: "height"
value {
f: 50
}
}
attr {
key: "orientation"
value {
placeholder: "N"
}
}
attr {
key: "type"
value {
placeholder: "MACRO"
}
}
attr {
key: "x"
value {
f: 150
}
}
attr {
key: "y"
value {
f: 250
}
}
attr {
key: "width"
value {
f: 50
}
}
}
node {
name: "M0/P1"
input: "M1/P1"
input: "M2/P1"
attr {
key: "macro_name"
value {
placeholder: "M0"
}
}
attr {
key: "type"
value {
placeholder: "MACRO_PIN"
}
}
attr {
key: "x_offset"
value {
f: -25
}
}
attr {
key: "y_offset"
value {
f: -25
}
}
attr {
key: "x"
value {
f: 325
}
}
attr {
key: "y"
value {
f: 325
}
}
attr {
key: "weight"
value {
f: 1000
}
}
}
node {
name: "M0/P2"
attr {
key: "macro_name"
value {
placeholder: "M0"
}
}
attr {
key: "type"
value {
placeholder: "MACRO_PIN"
}
}
attr {
key: "x_offset"
value {
f: -25
}
}
attr {
key: "y_offset"
value {
f: 25
}
}
attr {
key: "x"
value {
f: 325
}
}
attr {
key: "y"
value {
f: 375
}
}
attr {
key: "weight"
value {
f: 1000
}
}
}
node {
name: "M1/P1"
attr {
key: "macro_name"
value {
placeholder: "M1"
}
}
attr {
key: "type"
value {
placeholder: "MACRO_PIN"
}
}
attr {
key: "x_offset"
value {
f: 25
}
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attr {
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attr {
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node {
name: "M1/P2"
attr {
key: "macro_name"
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placeholder: "M1"
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attr {
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node {
name: "M2/P1"
attr {
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value {
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attr {
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attr {
key: "y"
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f: 225
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\ No newline at end of file
CodeElements/Plc_client/test/0P4M0m/netlist.pb.txt 0 → 100644
# proto-file: tensorflow/core/framework/graph.proto
# proto-message: tensorflow.GraphDef
node {
name: "M0"
attr {
key: "height"
value {
f: 50
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}
attr {
key: "orientation"
value {
placeholder: "N"
}
}
attr {
key: "type"
value {
placeholder: "MACRO"
}
}
attr {
key: "x"
value {
f: 350
}
}
attr {
key: "y"
value {
f: 350
}
}
attr {
key: "width"
value {
f: 50
}
}
}
node {
name: "M1"
attr {
key: "height"
value {
f: 50
}
}
attr {
key: "orientation"
value {
placeholder: "N"
}
}
attr {
key: "type"
value {
placeholder: "MACRO"
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}
attr {
key: "x"
value {
f: 50
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attr {
key: "y"
value {
f: 50
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attr {
key: "width"
value {
f: 50
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}
}
node {
name: "M2"
attr {
key: "height"
value {
f: 50
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}
attr {
key: "orientation"
value {
placeholder: "N"
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attr {
key: "type"
value {
placeholder: "MACRO"
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attr {
key: "x"
value {
f: 150
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attr {
key: "y"
value {
f: 250
}
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attr {
key: "width"
value {
f: 50
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}
}
node {
name: "M3"
attr {
key: "height"
value {
f: 50
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}
attr {
key: "orientation"
value {
placeholder: "N"
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}
attr {
key: "type"
value {
placeholder: "MACRO"
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attr {
key: "x"
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f: 250
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attr {
key: "y"
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attr {
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node {
name: "M0/P1"
input: "M1/P1"
input: "M2/P1"
input: "M3/P1"
attr {
key: "macro_name"
value {
placeholder: "M0"
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attr {
key: "type"
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attr {
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attr {
key: "y_offset"
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attr {
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attr {
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attr {
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node {
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attr {
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attr {
key: "type"
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attr {
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node {
name: "M1/P1"
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attr {
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attr {
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attr {
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attr {
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attr {
key: "y"
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node {
name: "M1/P2"
attr {
key: "macro_name"
value {
placeholder: "M1"
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attr {
key: "type"
value {
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attr {
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attr {
key: "y_offset"
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attr {
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node {
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attr {
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attr {
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attr {
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attr {
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attr {
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node {
name: "M3/P1"
attr {
key: "macro_name"
value {
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attr {
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attr {
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attr {
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attr {
key: "x"
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}
attr {
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value {
f: 125
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}
\ No newline at end of file
CodeElements/Plc_client/test/ariane133/initial.plc 0 → 100644
# Placement file for Circuit Training
# Source input file(s) : environment/test_data/ariane/ariane.txt
# This file : environment/test_data/ariane/init.plc
# Date : 2022-03-13 09:30:00
# Columns : 50 Rows : 50
# Width : 1599.99 Height : 1598.8
# Area : 1244102.4819999968
# Wirelength : 0.0
# Wirelength cost : 0.0
# Congestion cost : 0.0
# Block : ariane
# Routes per micron, hor : 70.33 ver : 74.51
# Routes used by macros, hor : 51.79 ver : 51.79
# Smoothing factor : 2
# Overlap threshold : 0.004
#s
#
#
# Counts of node types:
# HARD_MACROs : 133
# HARD_MACRO_PINs : 7847
# MACROs : 662
# MACRO_PINs : 24487
# PORTs : 495
# SOFT_MACROs : 529
# SOFT_MACRO_PINs : 16640
# STDCELLs : 0
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675 616.935 1354.67 N 0
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1815 1034.345 1354.67 N 0
1875 1093.975 1354.67 N 0
1935 1153.605 1354.67 N 0
1995 1213.235 1354.67 N 0
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20289 718.675 672.7 N 0
20318 708.7 606.9 N 0
20370 741.95 653.1 N 0
20410 731.12 688.1 N 0
20423 750.12 693.0 N 0
20435 770.07 571.9 N 0
20452 756.865 665.7 N 0
20472 699.865 581.0 N 0
20490 710.98 586.6 N 0
20505 756.77 681.8 N 0
20522 764.655 693.0 N 0
20536 778.62 567.0 N 0
20556 709.84 588.0 N 0
20572 741.855 685.3 N 0
20587 752.02 707.7 N 0
20600 719.91 581.7 N 0
20618 787.075 421.4 N 0
20660 625.1 321.3 N 0
20662 691.695 424.9 N 0
20676 748.125 293.3 N 0
20678 459.04 566.3 N 0
20715 477.185 567.0 N 0
20748 455.145 567.0 N 0
20778 499.89 480.9 N 0
20819 514.33 476.7 N 0
20859 823.08 335.3 N 0
20861 720.765 345.1 N 0
20910 829.35 306.6 N 0
21032 800.375 1152.2 N 0
21034 799.045 1101.8 N 0
21036 943.825 973.7 N 0
21038 914.565 1035.3 N 0
21040 719.245 424.2 N 0
21042 720.955 453.6 N 0
21067 520.98 548.1 N 0
21090 646.19 334.6 N 0
21092 520.41 590.1 N 0
21142 622.535 480.2 N 0
21144 685.14 508.2 N 0
21213 617.215 480.2 N 0
21215 686.47 316.4 N 0
21217 476.235 505.4 N 0
21235 480.89 505.4 N 0
21257 600.115 340.2 N 0
21259 570.285 480.2 N 0
21261 485.165 506.8 N 0
21326 623.865 448.0 N 0
21328 687.61 480.2 N 0
21330 427.595 946.4 N 0
21570 836.0 496.3 N 0
21677 454.67 570.5 N 0
21726 984.105 441.0 N 0
21819 1004.53 403.2 N 0
21916 734.54 728.7 N 0
22594 647.235 415.8 N 0
22652 652.08 847.0 N 0
22747 348.84 905.1 N 0
22848 618.165 753.2 N 0
23049 631.75 753.9 N 0
23201 891.1 1229.9 N 0
23331 556.13 841.4 N 0
23354 858.895 1229.9 N 0
23416 862.885 1204.7 N 0
23490 852.815 940.8 N 0
23523 883.215 1268.4 N 0
23585 807.025 496.3 N 0
23685 1038.16 518.0 N 0
23739 625.1 321.3 N 0
23849 748.125 293.3 N 0
23974 823.08 335.3 N 0
24090 800.375 1152.2 N 0
24234 799.045 1101.8 N 0
24381 943.825 973.7 N 0
24643 914.565 1035.3 N 0
24860 719.245 424.2 N 0
24981 646.19 334.6 N 0
25092 622.535 480.2 N 0
25159 617.215 480.2 N 0
25220 686.47 316.4 N 0
25344 600.115 340.2 N 0
25489 570.285 480.2 N 0
25537 623.865 448.0 N 0
25585 687.61 480.2 N 0
CodeElements/Plc_client/test/ariane133/netlist.pb.txt 0 → 100644
This source diff could not be displayed because it is too large. You can view the blob instead.
CodeElements/Plc_client/test/ariane_one_hard/initial.plc 0 → 100644
# Placement file for Circuit Training
# Source input file(s) : environment/test_data/ariane/netlist.pb.txt
# This file : environment/test_data/ariane/initial.plc
# Date : 2022-03-13 09:30:00
# Columns : 35 Rows : 33
# Width : 356.592 Height : 356.640
# Area : 99908.9764139
# Wirelength : 0.0
# Wirelength cost : 0.0
# Congestion cost : 0.0
# Block : ariane
# Routes per micron, hor : 70.33 ver : 74.51
# Routes used by macros, hor : 51.79 ver : 51.79
# Smoothing factor : 2
# Overlap threshold : 0.004
#
#
#
# Counts of node types:
# HARD_MACROs : 133
# HARD_MACRO_PINs : 11970
# MACROs : 932
# MACRO_PINs : 22802
# PORTs : 1231
# SOFT_MACROs : 799
# SOFT_MACRO_PINs : 10832
# STDCELLs : 0
#
# node_index x y orientation fixed
0 155.268 0.1975 - 1
1 216.676 0.1975 - 1
2 48.108 0.1975 - 1
3 200.032 0.1975 - 1
4 201.704 0.1975 - 1
5 202.388 0.1975 - 1
6 201.324 0.1975 - 1
7 203.072 0.1975 - 1
8 201.172 0.1975 - 1
9 203.984 0.1975 - 1
10 203.224 0.1975 - 1
11 203.832 0.1975 - 1
12 204.364 0.1975 - 1
13 204.212 0.1975 - 1
14 205.048 0.1975 - 1
15 206.036 0.1975 - 1
16 206.72 0.1975 - 1
17 213.408 0.1975 - 1
18 204.896 0.1975 - 1
19 212.952 0.1975 - 1
20 208.544 0.1975 - 1
21 209.988 0.1975 - 1
22 213.256 0.1975 - 1
23 210.368 0.1975 - 1
24 213.104 0.1975 - 1
25 211.432 0.1975 - 1
26 208.924 0.1975 - 1
27 209.076 0.1975 - 1
28 203.376 0.1975 - 1
29 205.2 0.1975 - 1
30 199.348 0.1975 - 1
31 200.26 0.1975 - 1
32 198.74 0.1975 - 1
33 199.5 0.1975 - 1
34 197.6 0.1975 - 1
35 199.88 0.1975 - 1
36 196.308 0.1975 - 1
37 199.652 0.1975 - 1
38 196.46 0.1975 - 1
39 196.992 0.1975 - 1
40 193.952 0.1975 - 1
41 195.7 0.1975 - 1
42 194.104 0.1975 - 1
43 195.852 0.1975 - 1
44 190.684 0.1975 - 1
45 192.812 0.1975 - 1
46 189.924 0.1975 - 1
47 193.8 0.1975 - 1
48 192.128 0.1975 - 1
49 191.824 0.1975 - 1
50 190.076 0.1975 - 1
51 196.156 0.1975 - 1
52 191.976 0.1975 - 1
53 196.004 0.1975 - 1
54 187.872 0.1975 - 1
55 195.396 0.1975 - 1
56 187.72 0.1975 - 1
57 195.548 0.1975 - 1
58 191.672 0.1975 - 1
59 191.064 0.1975 - 1
60 191.52 0.1975 - 1
61 187.568 0.1975 - 1
62 190.228 0.1975 - 1
63 186.124 0.1975 - 1
64 194.256 0.1975 - 1
65 196.612 0.1975 - 1
66 196.764 0.1975 - 1
67 172.216 0.1975 - 1
68 179.36 0.1975 - 1
69 182.4 0.1975 - 1
70 181.412 0.1975 - 1
71 178.22 0.1975 - 1
72 169.252 0.1975 - 1
73 178.372 0.1975 - 1
74 172.368 0.1975 - 1
75 178.524 0.1975 - 1
76 169.404 0.1975 - 1
77 76.608 0.226 - 1
78 88.92 0.1975 - 1
79 85.88 0.1975 - 1
80 105.84 0.177 - 1
81 77.9 0.1975 - 1
82 113.316 0.1975 - 1
83 75.924 0.1975 - 1
84 109.82 0.1975 - 1
85 63.536 0.1975 - 1
86 113.62 0.1975 - 1
87 76.836 0.1975 - 1
88 76.684 0.1975 - 1
89 99.484 0.1975 - 1
90 111.568 0.1975 - 1
91 107.856 0.177 - 1
92 76.988 0.1975 - 1
93 85.68 0.226 - 1
94 77.748 0.1975 - 1
95 85.576 0.1975 - 1
96 77.14 0.1975 - 1
97 103.824 0.226 - 1
98 85.196 0.1975 - 1
99 108.376 0.1975 - 1
100 77.292 0.1975 - 1
101 111.188 0.1975 - 1
102 60.192 0.1975 - 1
103 106.78 0.1975 - 1
104 87.932 0.1975 - 1
105 109.212 0.1975 - 1
106 80.028 0.1975 - 1
107 99.792 0.226 - 1
108 109.116 0.226 - 1
109 77.596 0.1975 - 1
110 100.472 0.1975 - 1
111 114.912 0.226 - 1
112 98.648 0.1975 - 1
113 114.912 0.1975 - 1
114 101.08 0.1975 - 1
115 77.444 0.1975 - 1
116 80.484 0.1975 - 1
117 66.12 0.1975 - 1
118 117.04 0.1975 - 1
119 60.344 0.1975 - 1
120 88.464 0.1975 - 1
121 112.328 0.1975 - 1
122 114.684 0.1975 - 1
123 106.092 0.177 - 1
124 98.496 0.1975 - 1
125 109.82 0.1975 - 1
126 66.728 0.1975 - 1
127 83.16 0.226 - 1
128 78.052 0.1975 - 1
129 91.728 0.226 - 1
130 113.62 0.1975 - 1
131 91.656 0.1975 - 1
132 92.188 0.1975 - 1
133 66.576 0.1975 - 1
134 112.176 0.1975 - 1
135 116.356 0.1975 - 1
136 78.624 0.177 - 1
137 93.252 0.1975 - 1
138 76.076 0.1975 - 1
139 79.724 0.1975 - 1
140 105.336 0.177 - 1
141 79.192 0.1975 - 1
142 78.432 0.1975 - 1
143 91.124 0.1975 - 1
144 78.28 0.1975 - 1
145 65.208 0.1975 - 1
146 84.168 0.226 - 1
147 106.722 0.177 - 1
148 109.668 0.1975 - 1
149 106.552 0.1975 - 1
150 109.516 0.1975 - 1
151 107.16 0.1975 - 1
152 116.128 0.1975 - 1
153 74.48 0.1975 - 1
154 63.232 0.1975 - 1
155 85.044 0.1975 - 1
156 84.512 0.1975 - 1
157 84.664 0.1975 - 1
158 63.84 0.1975 - 1
159 60.496 0.1975 - 1
160 106.096 0.1975 - 1
161 109.06 0.1975 - 1
162 85.5 0.1975 - 1
163 84.892 0.1975 - 1
164 99.636 0.1975 - 1
165 86.032 0.1975 - 1
166 104.576 0.1975 - 1
167 90.744 0.1975 - 1
168 88.084 0.1975 - 1
169 88.236 0.1975 - 1
170 86.412 0.1975 - 1
171 105.108 0.1975 - 1
172 79.876 0.1975 - 1
173 104.196 0.1975 - 1
174 76.228 0.1975 - 1
175 101.232 0.1975 - 1
176 96.9 0.1975 - 1
177 87.02 0.1975 - 1
178 93.936 0.1975 - 1
179 115.976 0.1975 - 1
180 115.824 0.1975 - 1
181 48.412 0.1975 - 1
182 111.112 0.1975 - 1
183 90.592 0.1975 - 1
184 67.032 0.1975 - 1
185 116.052 0.1975 - 1
186 100.928 0.1975 - 1
187 68.552 0.1975 - 1
188 68.4 0.1975 - 1
189 66.88 0.1975 - 1
190 106.248 0.1975 - 1
191 259.056 0.177 - 1
192 260.148 0.1975 - 1
193 261.212 0.1975 - 1
194 261.06 0.1975 - 1
195 261.82 0.1975 - 1
196 261.972 0.1975 - 1
197 259.16 0.1975 - 1
198 260.82 0.177 - 1
199 259.008 0.1975 - 1
200 259.16 0.1975 - 1
201 262.276 0.1975 - 1
202 259.312 0.1975 - 1
203 259.464 0.1975 - 1
204 257.108 0.1975 - 1
205 259.616 0.1975 - 1
206 259.768 0.1975 - 1
207 259.92 0.1975 - 1
208 263.036 0.1975 - 1
209 264.632 0.1975 - 1
210 266.228 0.1975 - 1
211 266.076 0.1975 - 1
212 265.924 0.1975 - 1
213 265.772 0.1975 - 1
214 265.164 0.1975 - 1
215 263.188 0.1975 - 1
216 260.604 0.1975 - 1
217 263.34 0.1975 - 1
218 260.452 0.1975 - 1
219 260.072 0.1975 - 1
220 263.644 0.1975 - 1
221 260.908 0.1975 - 1
222 263.796 0.1975 - 1
223 260.756 0.1975 - 1
224 264.404 0.1975 - 1
225 262.124 0.1975 - 1
226 265.62 0.1975 - 1
227 265.316 0.1975 - 1
228 264.024 0.1975 - 1
229 264.252 0.1975 - 1
230 263.492 0.1975 - 1
231 264.784 0.1975 - 1
232 265.012 0.1975 - 1
233 260.224 0.1975 - 1
234 265.468 0.1975 - 1
235 241.224 0.1975 - 1
236 247.76 0.1975 - 1
237 241.376 0.1975 - 1
238 241.528 0.1975 - 1
239 243.58 0.1975 - 1
240 247.608 0.1975 - 1
241 241.68 0.1975 - 1
242 247.456 0.1975 - 1
243 60.648 0.1975 - 1
244 110.628 0.226 - 1
245 71.364 0.1975 - 1
246 102.676 0.1975 - 1
247 105.26 0.1975 - 1
248 106.974 0.177 - 1
249 104.728 0.1975 - 1
250 104.88 0.1975 - 1
251 107.226 0.177 - 1
252 97.508 0.1975 - 1
253 88.704 0.226 - 1
254 107.856 0.226 - 1
255 80.56 0.1975 - 1
256 113.088 0.1975 - 1
257 60.8 0.1975 - 1
258 78.28 0.1975 - 1
259 86.184 0.1975 - 1
260 100.624 0.1975 - 1
261 88.2 0.177 - 1
262 78.736 0.1975 - 1
263 108.832 0.1975 - 1
264 104.424 0.1975 - 1
265 94.696 0.1975 - 1
266 79.04 0.1975 - 1
267 88.452 0.177 - 1
268 98.344 0.1975 - 1
269 88.16 0.1975 - 1
270 112.556 0.1975 - 1
271 80.18 0.1975 - 1
272 63.688 0.1975 - 1
273 78.888 0.1975 - 1
274 114.66 0.177 - 1
275 108.528 0.1975 - 1
276 88.312 0.1975 - 1
277 89.208 0.226 - 1
278 112.404 0.1975 - 1
279 64.372 0.1975 - 1
280 104.044 0.1975 - 1
281 77.824 0.1975 - 1
282 107.236 0.1975 - 1
283 85.12 0.1975 - 1
284 76.38 0.1975 - 1
285 93.556 0.1975 - 1
286 92.34 0.1975 - 1
287 254.904 0.1975 - 1
288 256.576 0.1975 - 1
289 67.944 0.1975 - 1
290 86.64 0.1975 - 1
291 90.72 0.226 - 1
292 89.908 0.1975 - 1
293 89.376 0.1975 - 1
294 92.72 0.1975 - 1
295 103.436 0.1975 - 1
296 92.036 0.1975 - 1
297 89.082 0.177 - 1
298 80.788 0.1975 - 1
299 86.336 0.1975 - 1
300 60.952 0.1975 - 1
301 103.892 0.1975 - 1
302 80.94 0.1975 - 1
303 88.616 0.1975 - 1
304 86.564 0.1975 - 1
305 90.896 0.1975 - 1
306 79.724 0.1975 - 1
307 81.244 0.1975 - 1
308 66.576 0.1975 - 1
309 110.96 0.1975 - 1
310 88.768 0.1975 - 1
311 81.092 0.1975 - 1
312 63 0.226 - 1
313 79.268 0.1975 - 1
314 81.396 0.1975 - 1
315 87.02 0.1975 - 1
316 80.18 0.1975 - 1
317 102.828 0.1975 - 1
318 91.124 0.1975 - 1
319 64.752 0.1975 - 1
320 88.704 0.177 - 1
321 63 0.177 - 1
322 106.932 0.1975 - 1
323 108.36 0.226 - 1
324 88.92 0.1975 - 1
325 108.224 0.1975 - 1
326 72.808 0.1975 - 1
327 81.624 0.1975 - 1
328 97.964 0.1975 - 1
329 91.884 0.1975 - 1
330 87.172 0.1975 - 1
331 119.624 0.1975 - 1
332 94.924 0.1975 - 1
333 89.072 0.1975 - 1
334 66.424 0.1975 - 1
335 110.124 0.226 - 1
336 97.902 0.177 - 1
337 99.18 0.1975 - 1
338 87.476 0.1975 - 1
339 93.784 0.1975 - 1
340 115.748 0.1975 - 1
341 81.776 0.1975 - 1
342 91.884 0.1975 - 1
343 76.532 0.1975 - 1
344 93.784 0.1975 - 1
345 111.132 0.226 - 1
346 89.224 0.1975 - 1
347 79.128 0.177 - 1
348 80.332 0.1975 - 1
349 68.248 0.1975 - 1
350 87.628 0.1975 - 1
351 85.348 0.1975 - 1
352 103.284 0.1975 - 1
353 96.596 0.1975 - 1
354 78.584 0.1975 - 1
355 87.948 0.226 - 1
356 87.78 0.1975 - 1
357 95.76 0.1975 - 1
358 107.844 0.1975 - 1
359 107.692 0.1975 - 1
360 85.652 0.1975 - 1
361 82.656 0.226 - 1
362 90.44 0.1975 - 1
363 93.24 0.226 - 1
364 48.716 0.1975 - 1
365 242.972 0.1975 - 1
366 243.124 0.1975 - 1
367 242.516 0.1975 - 1
368 243.428 0.1975 - 1
369 242.668 0.1975 - 1
370 242.82 0.1975 - 1
371 245.86 0.1975 - 1
372 248.824 0.1975 - 1
373 242.364 0.1975 - 1
374 243.276 0.1975 - 1
375 249.28 0.1975 - 1
376 245.176 0.1975 - 1
377 245.024 0.1975 - 1
378 245.328 0.1975 - 1
379 244.796 0.1975 - 1
380 244.644 0.1975 - 1
381 248.216 0.1975 - 1
382 240.616 0.1975 - 1
383 241.072 0.1975 - 1
384 248.368 0.1975 - 1
385 248.52 0.1975 - 1
386 240.464 0.1975 - 1
387 240.768 0.1975 - 1
388 240.92 0.1975 - 1
389 242.212 0.1975 - 1
390 240.008 0.1975 - 1
391 250.192 0.1975 - 1
392 240.16 0.1975 - 1
393 250.344 0.1975 - 1
394 241.908 0.1975 - 1
395 248.064 0.1975 - 1
396 247.912 0.1975 - 1
397 246.848 0.1975 - 1
398 247.304 0.1975 - 1
399 249.128 0.1975 - 1
400 247 0.1975 - 1
401 246.696 0.1975 - 1
402 245.708 0.1975 - 1
403 245.48 0.1975 - 1
404 244.492 0.1975 - 1
405 246.164 0.1975 - 1
406 246.012 0.1975 - 1
407 244.34 0.1975 - 1
408 247.152 0.1975 - 1
409 248.976 0.1975 - 1
410 246.316 0.1975 - 1
411 246.468 0.1975 - 1
412 244.188 0.1975 - 1
413 248.672 0.1975 - 1
414 250.648 0.1975 - 1
415 239.704 0.1975 - 1
416 250.496 0.1975 - 1
417 250.8 0.1975 - 1
418 239.856 0.1975 - 1
419 250.04 0.1975 - 1
420 242.06 0.1975 - 1
421 240.312 0.1975 - 1
422 243.732 0.1975 - 1
423 249.432 0.1975 - 1
424 243.884 0.1975 - 1
425 249.584 0.1975 - 1
426 244.036 0.1975 - 1
427 249.736 0.1975 - 1
428 249.888 0.1975 - 1
429 49.248 0.1975 - 1
430 49.552 0.1975 - 1
431 252.548 0.1975 - 1
432 49.856 0.1975 - 1
433 50.16 0.1975 - 1
434 50.692 0.1975 - 1
435 50.996 0.1975 - 1
436 51.3 0.1975 - 1
437 51.604 0.1975 - 1
438 52.136 0.1975 - 1
439 52.44 0.1975 - 1
440 52.744 0.1975 - 1
441 53.048 0.1975 - 1
442 53.58 0.1975 - 1
443 89.376 0.1975 - 1
444 252.396 0.1975 - 1
445 53.884 0.1975 - 1
446 54.188 0.1975 - 1
447 54.492 0.1975 - 1
448 54.644 0.1975 - 1
449 54.34 0.1975 - 1
450 54.036 0.1975 - 1
451 53.732 0.1975 - 1
452 53.428 0.1975 - 1
453 53.2 0.1975 - 1
454 52.896 0.1975 - 1
455 52.592 0.1975 - 1
456 52.288 0.1975 - 1
457 51.984 0.1975 - 1
458 103.132 0.1975 - 1
459 51.756 0.1975 - 1
460 89.68 0.1975 - 1
461 84.284 0.1975 - 1
462 90.288 0.1975 - 1
463 81.092 0.1975 - 1
464 115.9 0.1975 - 1
465 92.872 0.1975 - 1
466 82.46 0.1975 - 1
467 98.192 0.1975 - 1
468 112.024 0.1975 - 1
469 89.072 0.1975 - 1
470 105.588 0.177 - 1
471 87.948 0.177 - 1
472 115.292 0.1975 - 1
473 106.344 0.177 - 1
474 100.296 0.177 - 1
475 111.568 0.1975 - 1
476 113.24 0.1975 - 1
477 107.312 0.1975 - 1
478 107.844 0.1975 - 1
479 107.616 0.1975 - 1
480 114.532 0.1975 - 1
481 79.38 0.177 - 1
482 92.736 0.177 - 1
483 112.784 0.1975 - 1
484 114.408 0.177 - 1
485 107.996 0.1975 - 1
486 105.944 0.1975 - 1
487 108.756 0.1975 - 1
488 112.632 0.1975 - 1
489 88.616 0.1975 - 1
490 111.872 0.1975 - 1
491 104.58 0.177 - 1
492 95.76 0.177 - 1
493 112.644 0.177 - 1
494 114.156 0.177 - 1
495 112.392 0.177 - 1
496 99.792 0.177 - 1
497 112.48 0.1975 - 1
498 92.232 0.177 - 1
499 110.2 0.1975 - 1
500 100.044 0.177 - 1
501 93.024 0.1975 - 1
502 102.524 0.1975 - 1
503 102.22 0.1975 - 1
504 78.736 0.1975 - 1
505 102.372 0.1975 - 1
506 99.788 0.1975 - 1
507 91.602 0.177 - 1
508 86.436 0.177 - 1
509 109.364 0.1975 - 1
510 112.14 0.177 - 1
511 87.696 0.177 - 1
512 113.088 0.1975 - 1
513 111.888 0.177 - 1
514 110.808 0.1975 - 1
515 85.804 0.1975 - 1
516 67.792 0.1975 - 1
517 109.972 0.1975 - 1
518 103.572 0.177 - 1
519 91.352 0.1975 - 1
520 82.232 0.1975 - 1
521 85.68 0.177 - 1
522 79.632 0.177 - 1
523 83.98 0.1975 - 1
524 100.244 0.1975 - 1
525 79.04 0.1975 - 1
526 87.324 0.1975 - 1
527 92.492 0.1975 - 1
528 79.42 0.1975 - 1
529 86.716 0.1975 - 1
530 101.916 0.1975 - 1
531 115.444 0.1975 - 1
532 78.888 0.1975 - 1
533 110.352 0.1975 - 1
534 83.676 0.1975 - 1
535 62.496 0.226 - 1
536 90.972 0.1975 - 1
537 89.908 0.1975 - 1
538 75.772 0.1975 - 1
539 108.148 0.1975 - 1
540 86.688 0.177 - 1
541 83.524 0.1975 - 1
542 82.46 0.1975 - 1
543 83.372 0.1975 - 1
544 108.984 0.1975 - 1
545 108.528 0.1975 - 1
546 90.06 0.1975 - 1
547 82.612 0.1975 - 1
548 82.764 0.1975 - 1
549 82.992 0.1975 - 1
550 87.444 0.177 - 1
551 91.504 0.1975 - 1
552 61.236 0.177 - 1
553 81.32 0.1975 - 1
554 99.94 0.1975 - 1
555 102.98 0.1975 - 1
556 108.3 0.1975 - 1
557 79.572 0.1975 - 1
558 86.26 0.1975 - 1
559 64.524 0.1975 - 1
560 89.528 0.1975 - 1
561 88.768 0.1975 - 1
562 101.556 0.177 - 1
563 64.296 0.1975 - 1
564 78.128 0.1975 - 1
565 75.62 0.1975 - 1
566 101.808 0.177 - 1
567 90.09 0.177 - 1
568 87.192 0.177 - 1
569 113.904 0.177 - 1
570 90.342 0.177 - 1
571 61.104 0.1975 - 1
572 61.74 0.177 - 1
573 86.868 0.1975 - 1
574 110.656 0.1975 - 1
575 84.294 0.177 - 1
576 113.652 0.177 - 1
577 98.784 0.177 - 1
578 113.848 0.1975 - 1
579 102.816 0.226 - 1
580 99.332 0.1975 - 1
581 108.36 0.177 - 1
582 112.936 0.1975 - 1
583 108.108 0.177 - 1
584 102.816 0.177 - 1
585 107.464 0.1975 - 1
586 114.38 0.1975 - 1
587 97.052 0.1975 - 1
588 111.72 0.1975 - 1
589 98.154 0.177 - 1
590 102.828 0.1975 - 1
591 108.864 0.177 - 1
592 102.144 0.1975 - 1
593 109.116 0.177 - 1
594 98.344 0.1975 - 1
595 104.88 0.1975 - 1
596 111.796 0.1975 - 1
597 104.328 0.177 - 1
598 97.146 0.177 - 1
599 109.62 0.226 - 1
600 102.312 0.226 - 1
601 110.88 0.177 - 1
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13201 312.56 86.67 N 0
13202 311.427 48.15 N 0
13203 312.446 28.89 N 0
13204 316.315 279.27 N 0
13205 339.102 202.23 N 0
13206 275.824 242.801 N 0
13207 283.204 164.783 N 0
13208 334.681 260.01 N 0
13209 341.914 182.97 N 0
13210 246.176 261.084 N 0
13211 280.392 184.043 N 0
13212 313.296 67.41 N 0
13213 341.914 96.3945 N 0
13214 341.914 25.804 N 0
13215 305.18 260.01 N 0
13216 306.276 145.523 N 0
13217 275.53 262.061 N 0
13218 276.002 89.6095 N 0
13219 304.886 223.541 N 0
13220 338.293 163.71 N 0
13221 245.947 241.824 N 0
13222 255.343 126.263 N 0
13223 340.845 115.655 N 0
13224 341.801 45.064 N 0
13225 312.387 9.63 N 0
13226 339.102 221.49 N 0
13227 308.938 125.19 N 0
13228 280.392 203.303 N 0
13229 311.121 105.93 N 0
13230 334.435 240.75 N 0
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13299 221.682 179.605 N 0
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14628 183.885 38.587 N 0
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14837 160.057 105.841 N 0
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15914 290.217 23.4155 N 0
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16032 256.134 35.4134 N 0
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16529 81.3642 164.545 N 0
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16566 280.113 132.769 N 0
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16631 202.069 34.1973 N 0
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17031 163.666 34.5085 N 0
17045 286.913 38.2124 N 0
17060 264.638 8.87708 N 0
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17087 176.891 37.6752 N 0
17101 231.24 121.104 N 0
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17451 83.2094 137.012 N 0
17497 255.66 5.9245 N 0
17506 178.061 66.1047 N 0
17536 218.2 100.872 N 0
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17650 292.298 112.554 N 0
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17801 211.145 116.991 N 0
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18034 245.531 77.9532 N 0
18080 280.941 54.5856 N 0
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18401 221.153 7.4296 N 0
18417 281.048 144.651 N 0
18441 131.034 81.5541 N 0
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18511 281.583 55.051 N 0
18535 285.203 10.3101 N 0
18544 122.354 114.603 N 0
18588 178.532 67.2692 N 0
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18623 163.818 2.58909 N 0
18635 259.784 111.568 N 0
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18665 283.687 53.8695 N 0
18675 229.874 108.188 N 0
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18713 151.887 91.785 N 0
18738 273.409 105.13 N 0
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18810 182.196 24.4148 N 0
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18899 68.3603 140.864 N 0
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18914 206.076 9.76155 N 0
18923 152.663 72.8447 N 0
18950 293.904 63.691 N 0
18974 198.56 109 N 0
18991 240.622 69.649 N 0
19002 253.869 9.38462 N 0
19009 248.289 110.712 N 0
19025 167.689 34.9193 N 0
19048 256.493 36.0374 N 0
19069 214.596 23.541 N 0
19094 245.101 29.4621 N 0
19137 195.041 53.0575 N 0
19140 242.265 23.6546 N 0
19159 126.83 114.259 N 0
19193 237.076 68.0317 N 0
19224 213.852 62.8326 N 0
19241 251.631 55.8106 N 0
19259 220.686 101.805 N 0
19302 220.145 54.5468 N 0
19344 276.614 39.951 N 0
19357 204.666 69.2074 N 0
19388 264.859 67.8047 N 0
19413 243.587 20.9278 N 0
19433 170.717 71.6308 N 0
19474 203.223 98.3009 N 0
19489 138.522 27.3048 N 0
19497 185.415 49.24 N 0
19500 209.693 112.391 N 0
19521 291.07 62.2123 N 0
19528 261.42 35.3658 N 0
19544 197.405 13.9894 N 0
19552 295.046 83.0625 N 0
19564 171.172 35.7241 N 0
19576 234.528 69.285 N 0
19590 274.4 18.9775 N 0
19600 192.118 68.5477 N 0
19614 274.42 30.6377 N 0
19634 289.719 10.0873 N 0
19643 180.148 28.3612 N 0
19668 165.06 76.525 N 0
19695 276.26 61.3041 N 0
19703 149.413 111.78 N 0
19737 284.55 107.056 N 0
19757 144.641 65.798 N 0
19766 123.752 93.6217 N 0
19779 167.273 17.7213 N 0
19786 264.869 108.614 N 0
19793 286.237 144.826 N 0
19803 239.346 112.228 N 0
19830 276.684 47.9957 N 0
19838 152.203 101.775 N 0
19872 189.16 43.8103 N 0
19877 137.679 117.257 N 0
19891 239.314 63.4282 N 0
19905 204.956 20.4283 N 0
19914 132.293 110.267 N 0
19946 154.169 58.0537 N 0
19954 178.924 58.488 N 0
19964 248.891 114.323 N 0
19983 195.247 0.921335 N 0
19990 291.219 101.583 N 0
20008 163.471 48.0851 N 0
20015 145.664 14.8228 N 0
20020 192.094 23.8369 N 0
20029 289.208 150.301 N 0
20033 86.7249 147.841 N 0
20037 263.933 51.9614 N 0
20051 243.601 150.414 N 0
20069 150.512 53.6515 N 0
20102 74.6075 136.047 N 0
20133 252.065 36.6229 N 0
20157 152.405 39.8544 N 0
20171 200.478 99.1674 N 0
20183 205.356 49.129 N 0
20200 131.965 73.6945 N 0
20214 128.683 87.2541 N 0
20224 183.404 36.7942 N 0
20229 251.94 64.0439 N 0
20245 136.503 40.1655 N 0
20284 182.438 60.5181 N 0
20291 69.4255 138.073 N 0
20313 203.664 46.1529 N 0
20329 290.146 40.2365 N 0
20342 188.853 36.0832 N 0
20349 225.826 84.9731 N 0
20359 146.353 85.8275 N 0
20373 258.214 74.2176 N 0
20384 247.977 76.8299 N 0
20406 133.305 121.717 N 0
20413 245.609 145.192 N 0
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20432 223.897 46.3398 N 0
20486 214.496 44.2761 N 0
20501 275.577 37.3446 N 0
20509 256.069 93.4378 N 0
20543 189.957 28.8672 N 0
20550 177.64 35.491 N 0
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20629 125.882 75.6845 N 0
20642 149.054 33.6913 N 0
20666 248.999 51.5788 N 0
20685 234.068 105.066 N 0
20711 230.917 111.274 N 0
20734 209.581 10.6205 N 0
20752 220.122 7.95501 N 0
20768 229.191 6.65669 N 0
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20793 129.363 73.6963 N 0
20813 277.885 27.0031 N 0
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20863 252.615 111.653 N 0
20872 229.68 127.411 N 0
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20960 239.857 43.5393 N 0
20989 244.229 60.7131 N 0
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21026 56.2014 119.979 N 0
21053 288.978 98.4382 N 0
21097 172.849 4.92822 N 0
21110 263.892 49.4022 N 0
21125 187.372 14.7433 N 0
21135 262.773 43.584 N 0
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21221 268.147 28.417 N 0
21233 138.357 73.548 N 0
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21277 270.024 17.4334 N 0
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21325 288.92 6.87668 N 0
21336 251.558 63.0494 N 0
21342 258.895 74.0012 N 0
21346 206.829 67.0001 N 0
21358 91.4861 181.788 N 0
21364 276.136 62.9773 N 0
21388 293.277 98.5724 N 0
21393 125.305 87.1784 N 0
21409 199.613 112.159 N 0
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21443 251.08 87.3454 N 0
21502 240.234 36.8663 N 0
21523 212.074 61.0016 N 0
21533 155.205 17.3852 N 0
21536 90.1728 43.972 N 0
21548 281.467 144.689 N 0
21562 219.164 120.417 N 0
21575 232.986 12.6248 N 0
21587 255.314 34.125 N 0
21608 160.321 89.2124 N 0
21618 217.276 31.0699 N 0
21642 235.654 125.761 N 0
21651 257.823 39.6966 N 0
21671 212.219 77.7244 N 0
21704 258.377 113.074 N 0
21717 264.934 3.62742 N 0
21729 286.621 71.7632 N 0
21736 251.411 52.9238 N 0
21739 150.935 92.567 N 0
21757 162.121 58.6039 N 0
21765 241.147 141.187 N 0
21781 203.927 83.6488 N 0
21806 125.462 32.6635 N 0
21816 261.91 26.5491 N 0
21838 281.002 41.8882 N 0
21855 282.683 104.251 N 0
21874 198.789 43.077 N 0
21896 274.923 10.1134 N 0
21910 204.592 36.9727 N 0
21924 145.693 94.3025 N 0
21938 185.636 28.5363 N 0
21950 190.029 108.929 N 0
21959 261.296 28.5632 N 0
21987 196.951 100.349 N 0
21997 191.896 43.9142 N 0
22000 96.6128 41.5601 N 0
22010 261.748 67.6897 N 0
22056 196.921 78.4681 N 0
22065 262.127 67.669 N 0
22116 232.028 40.8122 N 0
22148 240.927 134.823 N 0
22152 217.83 34.8185 N 0
22159 234.233 59.9731 N 0
22190 228.587 12.945 N 0
22204 138.659 86.9706 N 0
22218 238 98.415 N 0
22232 164.282 49.6589 N 0
22236 256.025 84.8993 N 0
22256 186.426 1.2395 N 0
22263 228.783 27.4075 N 0
22287 195.652 28.9539 N 0
22295 89.5977 179.385 N 0
22302 280.404 63.7932 N 0
22310 254.188 12.6065 N 0
22320 208.36 48.497 N 0
22337 238.153 51.8061 N 0
22374 214.523 50.0751 N 0
22410 240.265 125.592 N 0
22421 253.301 87.6417 N 0
22441 227.183 94.3702 N 0
22449 206.201 86.9335 N 0
22462 212.158 117.757 N 0
22488 278.857 104.848 N 0
22510 259.415 34.8512 N 0
22525 279.878 135.085 N 0
22529 262.694 52.1419 N 0
22541 152.649 45.4453 N 0
22545 140.247 12.1334 N 0
22552 237.76 5.57448 N 0
22566 205.687 17.1988 N 0
22578 259.341 74.9569 N 0
22589 234.712 69.768 N 0
22601 209.918 13.7825 N 0
22644 229.799 54.3667 N 0
22679 225.846 68.7683 N 0
22717 219.296 24.6614 N 0
22736 229.603 17.0104 N 0
22765 206.548 105.955 N 0
22776 191.974 32.7395 N 0
22783 196.263 22.4924 N 0
22790 235.535 28.039 N 0
22806 277.972 76.1455 N 0
22813 184.705 42.3134 N 0
22820 296.344 144.84 N 0
22823 279.131 118.213 N 0
22826 296.516 49.868 N 0
22834 268.856 96.938 N 0
22847 296.641 92.96 N 0
22850 318.715 249.084 N 0
22853 297.482 182.227 N 0
22856 318.437 234.329 N 0
22859 324.456 118.83 N 0
22866 295.723 187.498 N 0
22869 326.533 104.15 N 0
22874 320.693 232.228 N 0
22877 121.31 165.781 N 0
22882 90.0297 232.414 N 0
22888 119.633 160.844 N 0
22891 82.5139 136.085 N 0
22899 116.125 246.684 N 0
22905 119.376 223.409 N 0
22910 96.5216 180.072 N 0
22914 123.329 114.741 N 0
22918 103.336 29.585 N 0
22927 88.6625 102.433 N 0
22930 71.0571 129.881 N 0
22939 176.158 174.48 N 0
22943 136.473 187.766 N 0
22947 146.037 121.586 N 0
22951 163.124 102.552 N 0
22969 89.9456 227.782 N 0
22974 30.6494 250.623 N 0
22977 58.596 116.935 N 0
22980 96.9415 258.19 N 0
22983 96.3225 240.757 N 0
22989 93.4832 177.411 N 0
22996 133.887 141.324 N 0
22999 147.962 173.69 N 0
23005 150.114 168.215 N 0
23008 89.8595 245.847 N 0
23014 95.3706 226.716 N 0
23020 84.7615 135.143 N 0
23023 175.713 147.981 N 0
23038 90.251 227.317 N 0
23044 92.6258 206.565 N 0
23048 86.9235 136.916 N 0
23051 114.656 25.6419 N 0
23056 88.179 40.2285 N 0
23059 105.932 19.38 N 0
23062 180.68 209.969 N 0
23067 87.6945 136.193 N 0
23070 89.7034 234.545 N 0
23076 89.6985 241.474 N 0
23081 63.156 162.488 N 0
23084 29.1855 80.3765 N 0
23087 88.179 90.583 N 0
23090 63.2851 270.317 N 0
23096 63.156 163 N 0
23099 29.241 84.7997 N 0
23104 105.769 267.069 N 0
23110 96.057 180.454 N 0
23114 134.503 142.837 N 0
23117 67.9077 273.144 N 0
23122 67.9055 162.7 N 0
23125 29.1533 84.764 N 0
23129 95.0324 249.159 N 0
23135 93.4845 179.714 N 0
23138 88.179 101.586 N 0
23141 66.1393 173.219 N 0
23146 89.6925 197.701 N 0
23151 84.9045 134.94 N 0
23154 124.041 189.567 N 0
23157 45.4184 23.3463 N 0
23247 46.88 38.4 N 0
23250 88.4722 94.4615 N 0
23314 107.794 19.6369 N 0
23385 45.2506 19.4634 N 0
23391 108.974 20.7018 N 0
23553 115.669 20.1873 N 0
23668 87.7177 159.068 N 0
23684 135.471 3.35201 N 0
23751 206.26 67.3404 N 0
23764 270.379 55.56 N 0
23767 119.318 46.8987 N 0
23783 270.577 130.337 N 0
23817 236.034 210.162 N 0
23835 296.485 191.697 N 0
23871 230.574 56.006 N 0
23881 300.541 136.147 N 0
23885 195.883 48.4849 N 0
23899 128.618 19.605 N 0
23902 298.12 75.394 N 0
23906 47.0225 38.4 N 0
23909 59.166 58.32 N 0
23912 129.732 78.12 N 0
23915 160.955 107.364 N 0
23919 146.388 129.125 N 0
23924 163.561 89.64 N 0
23927 163.96 43.5245 N 0
23931 117.506 47.665 N 0
23935 130.919 19.38 N 0
23938 146.176 142.8 N 0
23941 133.34 76.377 N 0
23944 121.436 44.8935 N 0
23947 209.576 6.50564 N 0
23953 268.248 107.905 N 0
23956 278.75 58.2546 N 0
23960 119.563 96.7765 N 0
23964 118.502 83.863 N 0
23967 128.351 110.795 N 0
23970 145.676 53.5585 N 0
23975 224.951 42.48 N 0
23978 160.901 36.7882 N 0
23987 114.348 25.3964 N 0
23992 277.689 58.0185 N 0
23996 291.279 94.0554 N 0
24000 90.5423 42.9421 N 0
24009 102.264 44.6535 N 0
24012 267.733 127.926 N 0
24016 217.632 74.581 N 0
24019 279.595 154.596 N 0
24022 314.636 157.267 N 0
24026 151.546 153.734 N 0
24032 148.038 185.598 N 0
24036 146.495 143.961 N 0
24039 159.859 149.372 N 0
24071 295.946 1.3842 N 0
24076 257.81 137.116 N 0
24085 187.081 66.8735 N 0
24088 241.002 58.2995 N 0
24093 88.2659 95.9705 N 0
24107 118.448 104.946 N 0
24111 119.301 77.52 N 0
24114 145.414 188.681 N 0
24121 180.554 210.827 N 0
24133 202.191 96.3633 N 0
24147 201.591 60.2579 N 0
24185 233.684 55.8567 N 0
24189 220.882 74.2135 N 0
24192 131.15 75.345 N 0
24195 146.436 144.713 N 0
24202 117.506 44.8935 N 0
24205 127.115 21.525 N 0
24212 129.516 72.7865 N 0
24215 117.506 49.838 N 0
24218 265.801 101.157 N 0
24222 93.998 41.5885 N 0
24236 45.0067 36.797 N 0
24254 132.363 14.831 N 0
24258 326.228 100.533 N 0
24263 179.294 44.88 N 0
24266 57.3156 125.472 N 0
24282 239.364 59.1843 N 0
24287 251.753 120.377 N 0
24293 249.346 88.0488 N 0
24301 125.212 132.205 N 0
24308 146.991 139.834 N 0
24311 54.183 115.767 N 0
24314 194.461 90.975 N 0
24317 298.595 106.92 N 0
24320 28.6425 25.32 N 0
24323 117.58 70.3568 N 0
24328 259.46 116.074 N 0
24332 266.473 99.428 N 0
24335 28.6425 24.36 N 0
24338 28.9275 82.439 N 0
24342 107.262 29.1454 N 0
24378 324.28 115.867 N 0
24381 82.8657 139.832 N 0
24393 204.932 46.434 N 0
24396 234.142 41.7002 N 0
24400 192.374 67.2547 N 0
24411 235.237 54.4498 N 0
24431 198.781 92.9365 N 0
24438 146.889 68.0145 N 0
24441 178.804 132.572 N 0
24446 145.773 44.486 N 0
24449 88.2635 91.831 N 0
24452 263.348 114.398 N 0
24456 146.234 141.987 N 0
24459 205.746 92.4722 N 0
24468 273.918 20.0129 N 0
24474 152.333 106.92 N 0
24477 154.46 67.35 N 0
24480 240.975 55.5985 N 0
24483 192.722 61.3014 N 0
24488 210.002 55.069 N 0
24502 204.969 96.0307 N 0
24519 290.096 154.297 N 0
24525 175.389 71.16 N 0
24528 245.691 53.6037 N 0
24537 265.852 30.724 N 0
24540 226.692 92.1438 N 0
24548 164.298 82.9279 N 0
24564 193.214 97.2629 N 0
24570 167.796 92.344 N 0
24574 198.786 86.6745 N 0
24577 169.161 90.7355 N 0
24580 135.038 112.483 N 0
24583 119.4 59.4692 N 0
24587 153.013 140.673 N 0
24594 227.59 20.256 N 0
24597 182.889 110.235 N 0
24600 142.731 141.451 N 0
24603 245.293 84.3375 N 0
24607 203.452 105.693 N 0
24616 85.9173 172.848 N 0
24628 238.435 171.101 N 0
24631 279.173 151.615 N 0
24634 110.566 22.6615 N 0
24691 59.4225 78.6 N 0
24694 260.421 84.053 N 0
24697 260.264 27.609 N 0
24700 184.492 71.475 N 0
24703 266.883 59.08 N 0
24708 132.208 76.282 N 0
24711 157.572 109.996 N 0
24714 270.135 135.773 N 0
24717 275.553 58.4955 N 0
24720 205.386 53.3227 N 0
24723 252.936 108.656 N 0
24726 261.368 97.1449 N 0
24736 224.535 29.3109 N 0
24748 117.506 49.838 N 0
24751 231.855 94.7505 N 0
24754 117.506 49.029 N 0
24757 203.549 125.371 N 0
24772 204.616 70.8295 N 0
24783 208.389 94.0126 N 0
24797 88.2616 84.9088 N 0
24813 58.482 127.08 N 0
24816 122.515 70.4995 N 0
24819 28.6425 35.88 N 0
24822 228.863 96.815 N 0
24828 218.499 120.752 N 0
24831 234.505 48.4731 N 0
24867 90.2053 39.7381 N 0
24886 291.436 89.726 N 0
24889 149.954 168.728 N 0
24892 146.775 121.693 N 0
24895 241.516 77.26 N 0
24898 63.954 58.32 N 0
24901 164.878 109.596 N 0
24904 252.453 61.32 N 0
24907 115.189 66.7431 N 0
24913 189.163 60.713 N 0
24916 216.915 92.4641 N 0
24927 227.94 110.435 N 0
24934 232.116 57.5212 N 0
24962 217.052 131.334 N 0
CodeElements/Plc_client/test/ariane_one_hard/netlist.pb.txt 0 → 100644
# proto-file: tensorflow/core/framework/graph.proto
# proto-message: tensorflow.GraphDef
node {
name: "i_ariane/ex_stage_i/lsu_i/i_nbdcache/sram_block_4__data_sram/mem/mem_inst_mem_256x128_256x16_0x1"
attr {
key: "height"
value {
f: 19.26
}
}
attr {
key: "orientation"
value {
placeholder: "N"
}
}
attr {
key: "type"
value {
placeholder: "MACRO"
}
}
attr {
key: "width"
value {
f: 29.355
}
}
attr {
key: "x"
value {
f: 0
}
}
attr {
key: "y"
value {
f: 0
}
}
}
\ No newline at end of file
CodeElements/Plc_client/test/ariane_overlap_MACRO/initial.plc 0 → 100644
# Placement file for Circuit Training
# Source input file(s) : environment/test_data/ariane/netlist.pb.txt
# This file : environment/test_data/ariane/initial.plc
# Date : 2022-03-13 09:30:00
# Columns : 35 Rows : 33
# Width : 356.592 Height : 356.640
# Area : 99908.9764139
# Wirelength : 0.0
# Wirelength cost : 0.0
# Congestion cost : 0.0
# Block : ariane
# Routes per micron, hor : 70.33 ver : 74.51
# Routes used by macros, hor : 51.79 ver : 51.79
# Smoothing factor : 2
# Overlap threshold : 0.004
#
#
#
# Counts of node types:
# HARD_MACROs : 133
# HARD_MACRO_PINs : 11970
# MACROs : 932
# MACRO_PINs : 22802
# PORTs : 1231
# SOFT_MACROs : 799
# SOFT_MACRO_PINs : 10832
# STDCELLs : 0
#
# node_index x y orientation fixed
0 155.268 0.1975 - 1
1 216.676 0.1975 - 1
2 48.108 0.1975 - 1
3 200.032 0.1975 - 1
4 201.704 0.1975 - 1
5 202.388 0.1975 - 1
6 201.324 0.1975 - 1
7 203.072 0.1975 - 1
8 201.172 0.1975 - 1
9 203.984 0.1975 - 1
10 203.224 0.1975 - 1
11 203.832 0.1975 - 1
12 204.364 0.1975 - 1
13 204.212 0.1975 - 1
14 205.048 0.1975 - 1
15 206.036 0.1975 - 1
16 206.72 0.1975 - 1
17 213.408 0.1975 - 1
18 204.896 0.1975 - 1
19 212.952 0.1975 - 1
20 208.544 0.1975 - 1
21 209.988 0.1975 - 1
22 213.256 0.1975 - 1
23 210.368 0.1975 - 1
24 213.104 0.1975 - 1
25 211.432 0.1975 - 1
26 208.924 0.1975 - 1
27 209.076 0.1975 - 1
28 203.376 0.1975 - 1
29 205.2 0.1975 - 1
30 199.348 0.1975 - 1
31 200.26 0.1975 - 1
32 198.74 0.1975 - 1
33 199.5 0.1975 - 1
34 197.6 0.1975 - 1
35 199.88 0.1975 - 1
36 196.308 0.1975 - 1
37 199.652 0.1975 - 1
38 196.46 0.1975 - 1
39 196.992 0.1975 - 1
40 193.952 0.1975 - 1
41 195.7 0.1975 - 1
42 194.104 0.1975 - 1
43 195.852 0.1975 - 1
44 190.684 0.1975 - 1
45 192.812 0.1975 - 1
46 189.924 0.1975 - 1
47 193.8 0.1975 - 1
48 192.128 0.1975 - 1
49 191.824 0.1975 - 1
50 190.076 0.1975 - 1
51 196.156 0.1975 - 1
52 191.976 0.1975 - 1
53 196.004 0.1975 - 1
54 187.872 0.1975 - 1
55 195.396 0.1975 - 1
56 187.72 0.1975 - 1
57 195.548 0.1975 - 1
58 191.672 0.1975 - 1
59 191.064 0.1975 - 1
60 191.52 0.1975 - 1
61 187.568 0.1975 - 1
62 190.228 0.1975 - 1
63 186.124 0.1975 - 1
64 194.256 0.1975 - 1
65 196.612 0.1975 - 1
66 196.764 0.1975 - 1
67 172.216 0.1975 - 1
68 179.36 0.1975 - 1
69 182.4 0.1975 - 1
70 181.412 0.1975 - 1
71 178.22 0.1975 - 1
72 169.252 0.1975 - 1
73 178.372 0.1975 - 1
74 172.368 0.1975 - 1
75 178.524 0.1975 - 1
76 169.404 0.1975 - 1
77 76.608 0.226 - 1
78 88.92 0.1975 - 1
79 85.88 0.1975 - 1
80 105.84 0.177 - 1
81 77.9 0.1975 - 1
82 113.316 0.1975 - 1
83 75.924 0.1975 - 1
84 109.82 0.1975 - 1
85 63.536 0.1975 - 1
86 113.62 0.1975 - 1
87 76.836 0.1975 - 1
88 76.684 0.1975 - 1
89 99.484 0.1975 - 1
90 111.568 0.1975 - 1
91 107.856 0.177 - 1
92 76.988 0.1975 - 1
93 85.68 0.226 - 1
94 77.748 0.1975 - 1
95 85.576 0.1975 - 1
96 77.14 0.1975 - 1
97 103.824 0.226 - 1
98 85.196 0.1975 - 1
99 108.376 0.1975 - 1
100 77.292 0.1975 - 1
101 111.188 0.1975 - 1
102 60.192 0.1975 - 1
103 106.78 0.1975 - 1
104 87.932 0.1975 - 1
105 109.212 0.1975 - 1
106 80.028 0.1975 - 1
107 99.792 0.226 - 1
108 109.116 0.226 - 1
109 77.596 0.1975 - 1
110 100.472 0.1975 - 1
111 114.912 0.226 - 1
112 98.648 0.1975 - 1
113 114.912 0.1975 - 1
114 101.08 0.1975 - 1
115 77.444 0.1975 - 1
116 80.484 0.1975 - 1
117 66.12 0.1975 - 1
118 117.04 0.1975 - 1
119 60.344 0.1975 - 1
120 88.464 0.1975 - 1
121 112.328 0.1975 - 1
122 114.684 0.1975 - 1
123 106.092 0.177 - 1
124 98.496 0.1975 - 1
125 109.82 0.1975 - 1
126 66.728 0.1975 - 1
127 83.16 0.226 - 1
128 78.052 0.1975 - 1
129 91.728 0.226 - 1
130 113.62 0.1975 - 1
131 91.656 0.1975 - 1
132 92.188 0.1975 - 1
133 66.576 0.1975 - 1
134 112.176 0.1975 - 1
135 116.356 0.1975 - 1
136 78.624 0.177 - 1
137 93.252 0.1975 - 1
138 76.076 0.1975 - 1
139 79.724 0.1975 - 1
140 105.336 0.177 - 1
141 79.192 0.1975 - 1
142 78.432 0.1975 - 1
143 91.124 0.1975 - 1
144 78.28 0.1975 - 1
145 65.208 0.1975 - 1
146 84.168 0.226 - 1
147 106.722 0.177 - 1
148 109.668 0.1975 - 1
149 106.552 0.1975 - 1
150 109.516 0.1975 - 1
151 107.16 0.1975 - 1
152 116.128 0.1975 - 1
153 74.48 0.1975 - 1
154 63.232 0.1975 - 1
155 85.044 0.1975 - 1
156 84.512 0.1975 - 1
157 84.664 0.1975 - 1
158 63.84 0.1975 - 1
159 60.496 0.1975 - 1
160 106.096 0.1975 - 1
161 109.06 0.1975 - 1
162 85.5 0.1975 - 1
163 84.892 0.1975 - 1
164 99.636 0.1975 - 1
165 86.032 0.1975 - 1
166 104.576 0.1975 - 1
167 90.744 0.1975 - 1
168 88.084 0.1975 - 1
169 88.236 0.1975 - 1
170 86.412 0.1975 - 1
171 105.108 0.1975 - 1
172 79.876 0.1975 - 1
173 104.196 0.1975 - 1
174 76.228 0.1975 - 1
175 101.232 0.1975 - 1
176 96.9 0.1975 - 1
177 87.02 0.1975 - 1
178 93.936 0.1975 - 1
179 115.976 0.1975 - 1
180 115.824 0.1975 - 1
181 48.412 0.1975 - 1
182 111.112 0.1975 - 1
183 90.592 0.1975 - 1
184 67.032 0.1975 - 1
185 116.052 0.1975 - 1
186 100.928 0.1975 - 1
187 68.552 0.1975 - 1
188 68.4 0.1975 - 1
189 66.88 0.1975 - 1
190 106.248 0.1975 - 1
191 259.056 0.177 - 1
192 260.148 0.1975 - 1
193 261.212 0.1975 - 1
194 261.06 0.1975 - 1
195 261.82 0.1975 - 1
196 261.972 0.1975 - 1
197 259.16 0.1975 - 1
198 260.82 0.177 - 1
199 259.008 0.1975 - 1
200 259.16 0.1975 - 1
201 262.276 0.1975 - 1
202 259.312 0.1975 - 1
203 259.464 0.1975 - 1
204 257.108 0.1975 - 1
205 259.616 0.1975 - 1
206 259.768 0.1975 - 1
207 259.92 0.1975 - 1
208 263.036 0.1975 - 1
209 264.632 0.1975 - 1
210 266.228 0.1975 - 1
211 266.076 0.1975 - 1
212 265.924 0.1975 - 1
213 265.772 0.1975 - 1
214 265.164 0.1975 - 1
215 263.188 0.1975 - 1
216 260.604 0.1975 - 1
217 263.34 0.1975 - 1
218 260.452 0.1975 - 1
219 260.072 0.1975 - 1
220 263.644 0.1975 - 1
221 260.908 0.1975 - 1
222 263.796 0.1975 - 1
223 260.756 0.1975 - 1
224 264.404 0.1975 - 1
225 262.124 0.1975 - 1
226 265.62 0.1975 - 1
227 265.316 0.1975 - 1
228 264.024 0.1975 - 1
229 264.252 0.1975 - 1
230 263.492 0.1975 - 1
231 264.784 0.1975 - 1
232 265.012 0.1975 - 1
233 260.224 0.1975 - 1
234 265.468 0.1975 - 1
235 241.224 0.1975 - 1
236 247.76 0.1975 - 1
237 241.376 0.1975 - 1
238 241.528 0.1975 - 1
239 243.58 0.1975 - 1
240 247.608 0.1975 - 1
241 241.68 0.1975 - 1
242 247.456 0.1975 - 1
243 60.648 0.1975 - 1
244 110.628 0.226 - 1
245 71.364 0.1975 - 1
246 102.676 0.1975 - 1
247 105.26 0.1975 - 1
248 106.974 0.177 - 1
249 104.728 0.1975 - 1
250 104.88 0.1975 - 1
251 107.226 0.177 - 1
252 97.508 0.1975 - 1
253 88.704 0.226 - 1
254 107.856 0.226 - 1
255 80.56 0.1975 - 1
256 113.088 0.1975 - 1
257 60.8 0.1975 - 1
258 78.28 0.1975 - 1
259 86.184 0.1975 - 1
260 100.624 0.1975 - 1
261 88.2 0.177 - 1
262 78.736 0.1975 - 1
263 108.832 0.1975 - 1
264 104.424 0.1975 - 1
265 94.696 0.1975 - 1
266 79.04 0.1975 - 1
267 88.452 0.177 - 1
268 98.344 0.1975 - 1
269 88.16 0.1975 - 1
270 112.556 0.1975 - 1
271 80.18 0.1975 - 1
272 63.688 0.1975 - 1
273 78.888 0.1975 - 1
274 114.66 0.177 - 1
275 108.528 0.1975 - 1
276 88.312 0.1975 - 1
277 89.208 0.226 - 1
278 112.404 0.1975 - 1
279 64.372 0.1975 - 1
280 104.044 0.1975 - 1
281 77.824 0.1975 - 1
282 107.236 0.1975 - 1
283 85.12 0.1975 - 1
284 76.38 0.1975 - 1
285 93.556 0.1975 - 1
286 92.34 0.1975 - 1
287 254.904 0.1975 - 1
288 256.576 0.1975 - 1
289 67.944 0.1975 - 1
290 86.64 0.1975 - 1
291 90.72 0.226 - 1
292 89.908 0.1975 - 1
293 89.376 0.1975 - 1
294 92.72 0.1975 - 1
295 103.436 0.1975 - 1
296 92.036 0.1975 - 1
297 89.082 0.177 - 1
298 80.788 0.1975 - 1
299 86.336 0.1975 - 1
300 60.952 0.1975 - 1
301 103.892 0.1975 - 1
302 80.94 0.1975 - 1
303 88.616 0.1975 - 1
304 86.564 0.1975 - 1
305 90.896 0.1975 - 1
306 79.724 0.1975 - 1
307 81.244 0.1975 - 1
308 66.576 0.1975 - 1
309 110.96 0.1975 - 1
310 88.768 0.1975 - 1
311 81.092 0.1975 - 1
312 63 0.226 - 1
313 79.268 0.1975 - 1
314 81.396 0.1975 - 1
315 87.02 0.1975 - 1
316 80.18 0.1975 - 1
317 102.828 0.1975 - 1
318 91.124 0.1975 - 1
319 64.752 0.1975 - 1
320 88.704 0.177 - 1
321 63 0.177 - 1
322 106.932 0.1975 - 1
323 108.36 0.226 - 1
324 88.92 0.1975 - 1
325 108.224 0.1975 - 1
326 72.808 0.1975 - 1
327 81.624 0.1975 - 1
328 97.964 0.1975 - 1
329 91.884 0.1975 - 1
330 87.172 0.1975 - 1
331 119.624 0.1975 - 1
332 94.924 0.1975 - 1
333 89.072 0.1975 - 1
334 66.424 0.1975 - 1
335 110.124 0.226 - 1
336 97.902 0.177 - 1
337 99.18 0.1975 - 1
338 87.476 0.1975 - 1
339 93.784 0.1975 - 1
340 115.748 0.1975 - 1
341 81.776 0.1975 - 1
342 91.884 0.1975 - 1
343 76.532 0.1975 - 1
344 93.784 0.1975 - 1
345 111.132 0.226 - 1
346 89.224 0.1975 - 1
347 79.128 0.177 - 1
348 80.332 0.1975 - 1
349 68.248 0.1975 - 1
350 87.628 0.1975 - 1
351 85.348 0.1975 - 1
352 103.284 0.1975 - 1
353 96.596 0.1975 - 1
354 78.584 0.1975 - 1
355 87.948 0.226 - 1
356 87.78 0.1975 - 1
357 95.76 0.1975 - 1
358 107.844 0.1975 - 1
359 107.692 0.1975 - 1
360 85.652 0.1975 - 1
361 82.656 0.226 - 1
362 90.44 0.1975 - 1
363 93.24 0.226 - 1
364 48.716 0.1975 - 1
365 242.972 0.1975 - 1
366 243.124 0.1975 - 1
367 242.516 0.1975 - 1
368 243.428 0.1975 - 1
369 242.668 0.1975 - 1
370 242.82 0.1975 - 1
371 245.86 0.1975 - 1
372 248.824 0.1975 - 1
373 242.364 0.1975 - 1
374 243.276 0.1975 - 1
375 249.28 0.1975 - 1
376 245.176 0.1975 - 1
377 245.024 0.1975 - 1
378 245.328 0.1975 - 1
379 244.796 0.1975 - 1
380 244.644 0.1975 - 1
381 248.216 0.1975 - 1
382 240.616 0.1975 - 1
383 241.072 0.1975 - 1
384 248.368 0.1975 - 1
385 248.52 0.1975 - 1
386 240.464 0.1975 - 1
387 240.768 0.1975 - 1
388 240.92 0.1975 - 1
389 242.212 0.1975 - 1
390 240.008 0.1975 - 1
391 250.192 0.1975 - 1
392 240.16 0.1975 - 1
393 250.344 0.1975 - 1
394 241.908 0.1975 - 1
395 248.064 0.1975 - 1
396 247.912 0.1975 - 1
397 246.848 0.1975 - 1
398 247.304 0.1975 - 1
399 249.128 0.1975 - 1
400 247 0.1975 - 1
401 246.696 0.1975 - 1
402 245.708 0.1975 - 1
403 245.48 0.1975 - 1
404 244.492 0.1975 - 1
405 246.164 0.1975 - 1
406 246.012 0.1975 - 1
407 244.34 0.1975 - 1
408 247.152 0.1975 - 1
409 248.976 0.1975 - 1
410 246.316 0.1975 - 1
411 246.468 0.1975 - 1
412 244.188 0.1975 - 1
413 248.672 0.1975 - 1
414 250.648 0.1975 - 1
415 239.704 0.1975 - 1
416 250.496 0.1975 - 1
417 250.8 0.1975 - 1
418 239.856 0.1975 - 1
419 250.04 0.1975 - 1
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1135 82.656 0.177 - 1
1136 92.232 0.226 - 1
1137 82.908 0.177 - 1
1138 98.406 0.177 - 1
1139 95.38 0.1975 - 1
1140 97.398 0.177 - 1
1141 94.752 0.226 - 1
1142 95.76 0.226 - 1
1143 95.256 0.226 - 1
1144 94.248 0.226 - 1
1145 98.784 0.226 - 1
1146 96.894 0.177 - 1
1147 87.444 0.226 - 1
1148 86.94 0.226 - 1
1149 81.648 0.226 - 1
1150 82.152 0.226 - 1
1151 83.16 0.177 - 1
1152 83.79 0.177 - 1
1153 80.636 0.1975 - 1
1154 80.94 0.1975 - 1
1155 81.928 0.1975 - 1
1156 81.472 0.1975 - 1
1157 80.788 0.1975 - 1
1158 82.08 0.1975 - 1
1159 81.648 0.177 - 1
1160 81.9 0.177 - 1
1161 82.152 0.177 - 1
1162 83.22 0.1975 - 1
1163 84.132 0.1975 - 1
1164 84.042 0.177 - 1
1165 86.058 0.177 - 1
1166 85.05 0.177 - 1
1167 92.736 0.226 - 1
1168 73.492 0.1975 - 1
1169 94.392 0.1975 - 1
1170 94.544 0.1975 - 1
1171 76.532 0.1975 - 1
1172 72.96 0.1975 - 1
1173 71.516 0.1975 - 1
1174 71.668 0.1975 - 1
1175 76.38 0.1975 - 1
1176 71.82 0.1975 - 1
1177 71.972 0.1975 - 1
1178 76.076 0.1975 - 1
1179 93.492 0.177 - 1
1180 94.122 0.177 - 1
1181 93.744 0.226 - 1
1182 98.028 0.226 - 1
1183 96.012 0.177 - 1
1184 92.484 0.177 - 1
1185 93.744 0.177 - 1
1186 92.988 0.177 - 1
1187 72.124 0.1975 - 1
1188 69.996 0.1975 - 1
1189 71.136 0.1975 - 1
1190 70.984 0.1975 - 1
1191 72.58 0.1975 - 1
1192 72.276 0.1975 - 1
1193 73.112 0.1975 - 1
1194 72.428 0.1975 - 1
1195 43.928 0.1975 - 1
1196 43.624 0.1975 - 1
1197 63.612 0.1975 - 1
1198 43.32 0.1975 - 1
1199 43.092 0.1975 - 1
1200 42.788 0.1975 - 1
1201 42.484 0.1975 - 1
1202 42.18 0.1975 - 1
1203 41.876 0.1975 - 1
1204 41.572 0.1975 - 1
1205 41.344 0.1975 - 1
1206 41.04 0.1975 - 1
1207 40.736 0.1975 - 1
1208 40.432 0.1975 - 1
1209 67.792 0.1975 - 1
1210 67.184 0.1975 - 1
1211 40.128 0.1975 - 1
1212 39.9 0.1975 - 1
1213 39.596 0.1975 - 1
1214 39.292 0.1975 - 1
1215 38.988 0.1975 - 1
1216 38.684 0.1975 - 1
1217 38.456 0.1975 - 1
1218 38.152 0.1975 - 1
1219 37.848 0.1975 - 1
1220 37.544 0.1975 - 1
1221 37.24 0.1975 - 1
1222 37.012 0.1975 - 1
1223 36.708 0.1975 - 1
1224 63.992 0.1975 - 1
1225 64.98 0.1975 - 1
1226 178.752 0.1975 - 1
1227 176.244 0.1975 - 1
1228 177.46 0.1975 - 1
1229 175.408 0.1975 - 1
1230 176.396 0.1975 - 1
13201 312.56 86.67 N 0
13202 311.427 48.15 N 0
13203 312.446 28.89 N 0
13204 316.315 279.27 N 0
13205 339.102 202.23 N 0
13206 275.824 242.801 N 0
13207 283.204 164.783 N 0
13208 334.681 260.01 N 0
13209 341.914 182.97 N 0
13210 246.176 261.084 N 0
13211 280.392 184.043 N 0
13212 313.296 67.41 N 0
13213 341.914 96.3945 N 0
13214 341.914 25.804 N 0
13215 305.18 260.01 N 0
13216 306.276 145.523 N 0
13217 275.53 262.061 N 0
13218 276.002 89.6095 N 0
13219 304.886 223.541 N 0
13220 338.293 163.71 N 0
13221 245.947 241.824 N 0
13222 255.343 126.263 N 0
13223 340.845 115.655 N 0
13224 341.801 45.064 N 0
13225 312.387 9.63 N 0
13226 339.102 221.49 N 0
13227 308.938 125.19 N 0
13228 280.392 203.303 N 0
13229 311.121 105.93 N 0
13230 334.435 240.75 N 0
13231 336.435 144.45 N 0
13232 251.037 203.303 N 0
13233 263.221 145.523 N 0
13234 341.94 80.429 N 0
13235 342.005 61.334 N 0
13236 341.75 8.8835 N 0
13237 310.058 221.211 N 0
13238 310.892 163.978 N 0
13239 275.16 222.564 N 0
13240 251.037 184.043 N 0
13241 309.746 202.23 N 0
13242 312.56 182.97 N 0
13243 245.796 222.564 N 0
13244 253.85 164.783 N 0
13245 74.907 260.01 N 0
13246 133.617 199.473 N 0
13247 132.098 59.588 N 0
13248 102.743 131.443 N 0
13249 141.417 263.497 N 0
13250 132.965 177.525 N 0
13251 179.206 12.269 N 0
13252 102.743 112.183 N 0
13253 162.972 218.125 N 0
13254 179.669 100.485 N 0
13255 162.972 198.865 N 0
13256 16.1975 240.75 N 0
13257 16.1975 221.49 N 0
13258 32.4305 9.63 N 0
13259 14.6775 144.449 N 0
13260 112.061 279.27 N 0
13261 107.075 150.704 N 0
13262 73.3875 48.15 N 0
13263 44.0325 86.67 N 0
13264 141.417 244.237 N 0
13265 161.452 139.005 N 0
13266 162.972 179.605 N 0
13267 53.3515 298.53 N 0
13268 74.907 221.49 N 0
13269 91.1405 9.63 N 0
13270 73.3875 105.93 N 0
13271 112.061 235.806 N 0
13272 136.43 158.265 N 0
13273 122.926 35.1435 N 0
13274 103.454 92.9235 N 0
13275 184.513 256.645 N 0
13276 190.807 121.825 N 0
13277 192.327 179.605 N 0
13278 45.552 240.75 N 0
13279 77.7195 150.704 N 0
13280 149.85 15.8835 N 0
13281 102.743 73.664 N 0
13282 104.262 216.546 N 0
13283 132.098 131.443 N 0
13284 120.495 9.63 N 0
13285 102.743 54.4035 N 0
13286 200.126 218.125 N 0
13287 183.952 81.225 N 0
13288 195.139 160.344 N 0
13289 45.552 260.009 N 0
13290 14.6775 163.709 N 0
13291 14.6775 28.89 N 0
13292 44.0325 105.93 N 0
13293 74.907 240.75 N 0
13294 74.907 202.23 N 0
13295 44.0325 48.15 N 0
13296 73.3875 86.67 N 0
13297 221.682 198.865 N 0
13298 224.495 141.084 N 0
13299 221.682 179.605 N 0
13300 23.996 279.27 N 0
13301 16.1975 202.23 N 0
13302 14.6775 68.771 N 0
13303 44.0325 144.449 N 0
13304 141.417 282.758 N 0
13305 44.9005 182.969 N 0
13306 44.0325 67.41 N 0
13307 14.6775 125.19 N 0
13308 214.042 256.645 N 0
13309 246.646 98.4 N 0
13310 192.327 198.865 N 0
13311 53.3515 317.79 N 0
13312 15.5455 182.969 N 0
13313 14.6775 49.5115 N 0
13314 73.3875 125.19 N 0
13315 82.706 279.27 N 0
13316 45.552 202.23 N 0
13317 61.7855 28.89 N 0
13318 14.6775 105.93 N 0
13319 170.771 237.385 N 0
13320 161.452 119.746 N 0
13321 165.785 160.344 N 0
13322 45.552 221.49 N 0
13323 104.262 196.786 N 0
13324 61.7855 9.63 N 0
13325 48.3645 163.709 N 0
13326 103.61 169.964 N 0
13327 74.2555 182.969 N 0
13328 91.1405 28.89 N 0
13329 73.3875 67.41 N 0
13330 202.511 237.385 N 0
13331 195.139 141.084 N 0
13332 224.495 160.344 N 0
13333 133.758 100.485 N 0
13334 314.774 37.7577 N 0
13354 321.319 150.392 N 0
13357 261.626 236.421 N 0
13376 296.244 176.092 N 0
13386 283.374 138.768 N 0
13402 326.521 53.7131 N 0
13425 326.608 41.6708 N 0
13434 321.074 137.556 N 0
13472 290.107 130.575 N 0
13476 326.595 49.2981 N 0
13492 326.276 54.0355 N 0
13495 321.519 140.008 N 0
13499 284.779 224.782 N 0
13510 293.144 142.187 N 0
13517 326.511 51.92 N 0
13521 322 153.348 N 0
13530 269.47 232.643 N 0
13540 278.592 154.63 N 0
13543 123.101 127.011 N 0
13558 117.969 71.3248 N 0
13563 88.962 87.4022 N 0
13592 88.4983 86.1273 N 0
13613 86.4914 138.08 N 0
13619 120.142 162.016 N 0
13624 82.4604 151.047 N 0
13642 87.6245 140.205 N 0
13645 80.9853 135.647 N 0
13653 95.5688 206.634 N 0
13656 96.8993 41.5312 N 0
13666 88.4369 100.588 N 0
13679 150.993 168.362 N 0
13682 175.763 140.293 N 0
13696 177.169 146.562 N 0
13705 87.3942 161.363 N 0
13709 92.0591 205.221 N 0
13715 174.78 147.58 N 0
13721 91.0729 206.703 N 0
13727 73.7316 148.143 N 0
13734 79.0765 104.493 N 0
13746 179.82 139.149 N 0
13755 29.0271 78.8238 N 0
13764 68.6123 130.151 N 0
13771 83.272 151.49 N 0
13787 88.7318 175.028 N 0
13798 77.3018 149.05 N 0
13815 88.5304 39.3821 N 0
13829 179.826 140.706 N 0
13835 94.6554 41.9804 N 0
13866 100.606 31.389 N 0
13933 108.264 22.5057 N 0
14088 106.277 34.2939 N 0
14093 118.846 57.1702 N 0
14098 124.433 41.798 N 0
14103 203.827 40.7688 N 0
14144 311.024 46.0592 N 0
14159 273.664 10.9078 N 0
14179 200.462 22.8211 N 0
14194 295.056 42.5647 N 0
14235 263.511 27.3878 N 0
14247 278.378 8.02347 N 0
14263 218.066 117.565 N 0
14267 108.106 44.398 N 0
14270 118.231 21.8689 N 0
14280 277.564 9.57611 N 0
14306 119.573 111.528 N 0
14341 118.803 96.819 N 0
14371 123.97 142.269 N 0
14402 124.989 143.425 N 0
14407 280.091 22.6121 N 0
14421 273.786 33.6657 N 0
14426 175.256 141.003 N 0
14433 201.572 7.41756 N 0
14450 119.908 21.8108 N 0
14454 288.025 144.486 N 0
14484 142.959 109.375 N 0
14496 206.947 91.8626 N 0
14514 212.974 45.9913 N 0
14561 263.428 45.2239 N 0
14584 215.057 14.4005 N 0
14591 282.982 13.0023 N 0
14608 151.44 78.4743 N 0
14628 183.885 38.587 N 0
14637 278.034 109.528 N 0
14670 250.361 62.409 N 0
14678 219.423 16.9586 N 0
14684 271.133 114.807 N 0
14720 203.603 3.43601 N 0
14731 276.635 74.5329 N 0
14746 82.7451 138.678 N 0
14798 276.261 64.1613 N 0
14814 264.593 39.4665 N 0
14837 160.057 105.841 N 0
14851 293.505 80.0543 N 0
14858 189.5 55.1511 N 0
14868 289.422 120.227 N 0
14897 207.974 22.3375 N 0
14928 123.263 44.6681 N 0
14943 292.286 68.3698 N 0
14954 298.106 134.262 N 0
14977 248.093 49.4194 N 0
14986 257.367 66.8619 N 0
15021 290.554 28.637 N 0
15035 269.327 107.408 N 0
15070 256.779 71.6205 N 0
15109 69.1817 135.071 N 0
15161 173.702 36.7422 N 0
15185 292.748 62.7555 N 0
15200 120.407 82.3172 N 0
15241 294.299 61.1636 N 0
15247 248.357 18.5064 N 0
15264 228.846 38.0744 N 0
15292 203.368 35.4342 N 0
15321 90.0041 41.9847 N 0
15335 221.061 129.12 N 0
15348 196.496 63.8841 N 0
15364 134.505 72.2374 N 0
15375 258.909 7.96426 N 0
15380 284.392 71.3934 N 0
15396 233.669 24.042 N 0
15424 263.901 6.28865 N 0
15434 279.068 64.5494 N 0
15441 256.901 26.5584 N 0
15457 151.058 67.7897 N 0
15467 152.034 1.88292 N 0
15471 213.957 121.202 N 0
15477 160.705 71.1757 N 0
15512 275.797 49.3043 N 0
15523 174.076 49.8379 N 0
15539 284.454 114.128 N 0
15547 166.535 57.0731 N 0
15586 220.818 85.8166 N 0
15601 250.079 33.7099 N 0
15622 195.765 10.8115 N 0
15643 258.356 66.3535 N 0
15699 248.089 8.66958 N 0
15709 280.062 126.784 N 0
15719 272.133 54.6569 N 0
15735 64.9814 132.086 N 0
15770 221.057 62.7469 N 0
15790 88.2288 98.3159 N 0
15826 195.164 24.4106 N 0
15838 265.999 35.3773 N 0
15848 242.373 15.7005 N 0
15859 279.794 119.871 N 0
15883 166.081 0.978053 N 0
15892 149.483 177.481 N 0
15914 290.217 23.4155 N 0
15929 217.043 37.495 N 0
15943 156.708 96.9843 N 0
15979 105.92 42.2031 N 0
16009 208.59 29.0588 N 0
16032 256.134 35.4134 N 0
16056 279.128 125.191 N 0
16066 270.74 77.5424 N 0
16071 202.765 3.81193 N 0
16088 285.642 17.5391 N 0
16094 196.883 54.1249 N 0
16111 156.914 30.3136 N 0
16123 203.776 88.4984 N 0
16133 249.608 61.8461 N 0
16147 241.436 20.5544 N 0
16155 293.178 55.5756 N 0
16188 138.063 9.63134 N 0
16195 124.274 87.6154 N 0
16218 294.919 88.2857 N 0
16236 275.557 74.8013 N 0
16248 154.282 102.048 N 0
16266 293.221 37.0144 N 0
16277 198.143 104.689 N 0
16289 283.444 112.218 N 0
16299 275.989 17.7764 N 0
16307 283.836 25.0159 N 0
16333 203.437 43.5399 N 0
16347 217.556 10.5572 N 0
16360 167.76 148.282 N 0
16376 144.79 42.2492 N 0
16403 195.682 54.7911 N 0
16410 283.952 27.5284 N 0
16416 177.058 133.951 N 0
16423 229.138 65.3537 N 0
16475 235.098 133.017 N 0
16487 217.205 41.1967 N 0
16503 131.792 117.236 N 0
16507 211.184 18.8722 N 0
16524 239.045 6.56516 N 0
16529 81.3642 164.545 N 0
16539 238.16 52.5549 N 0
16566 280.113 132.769 N 0
16591 88.7363 42.441 N 0
16608 211.029 121.066 N 0
16614 247.45 111.157 N 0
16631 202.069 34.1973 N 0
16652 251.281 113.338 N 0
16657 287.638 72.0191 N 0
16666 158.459 98.2842 N 0
16679 285.808 79.5223 N 0
16687 220.186 74.1283 N 0
16716 119.495 118.394 N 0
16731 289.903 125.288 N 0
16740 174.305 25.1525 N 0
16745 101.862 40.9996 N 0
16758 138.818 86.5224 N 0
16776 291.784 100.759 N 0
16788 294.862 48.0326 N 0
16858 84.1545 171.17 N 0
16865 140.929 33.3266 N 0
16909 142.262 28.7526 N 0
16921 153.916 81.6638 N 0
16929 149.004 77.07 N 0
16944 266.252 56.2054 N 0
16985 140.26 80.1057 N 0
16995 216.789 29.9003 N 0
17031 163.666 34.5085 N 0
17045 286.913 38.2124 N 0
17060 264.638 8.87708 N 0
17071 163.805 76.6903 N 0
17076 160.43 79.1703 N 0
17087 176.891 37.6752 N 0
17101 231.24 121.104 N 0
17119 290.624 76.0678 N 0
17131 198.824 31.1209 N 0
17149 245.458 21.8062 N 0
17173 279.568 105.607 N 0
17233 243.543 11.1228 N 0
17245 185.36 45.756 N 0
17249 122.149 73.1676 N 0
17279 260.176 33.2729 N 0
17287 118.915 118.275 N 0
17308 114.55 44.5859 N 0
17317 164.215 46.2941 N 0
17323 277.277 115.703 N 0
17338 275.859 61.0028 N 0
17349 201.092 13.9408 N 0
17374 216.521 2.21545 N 0
17385 153.786 111.207 N 0
17392 224.56 79.4501 N 0
17407 173.518 56.0834 N 0
17418 238.704 115.355 N 0
17433 162.574 19.2913 N 0
17442 161.891 61.5188 N 0
17451 83.2094 137.012 N 0
17497 255.66 5.9245 N 0
17506 178.061 66.1047 N 0
17536 218.2 100.872 N 0
17542 281.595 150.703 N 0
17555 263.672 56.6759 N 0
17595 279.533 111.053 N 0
17610 274.853 47.5413 N 0
17619 278.413 71.9599 N 0
17650 292.298 112.554 N 0
17672 288.326 46.1023 N 0
17686 269.798 45.5466 N 0
17696 260.408 108.471 N 0
17709 253.919 59.986 N 0
17738 255.688 46.9206 N 0
17746 236.726 79.7562 N 0
17765 217.641 21.0124 N 0
17777 92.8228 56.8819 N 0
17781 147.46 88.4644 N 0
17801 211.145 116.991 N 0
17813 216.721 44.4611 N 0
17828 274.08 126.683 N 0
17843 240.582 68.152 N 0
17882 140.914 115.078 N 0
17889 233.234 107.717 N 0
17897 184.363 46.8156 N 0
17906 282.426 125.422 N 0
17920 92.2286 183.213 N 0
17927 213.181 5.20747 N 0
17942 63.8737 141.284 N 0
17957 243.968 7.32129 N 0
17968 247.214 76.0835 N 0
17988 187.405 57.1288 N 0
17998 182.097 56.4272 N 0
18017 292.892 22.9832 N 0
18034 245.531 77.9532 N 0
18080 280.941 54.5856 N 0
18084 248.141 74.6745 N 0
18116 285.267 24.6111 N 0
18143 277.212 3.7123 N 0
18154 289.643 135.209 N 0
18168 223.867 110.015 N 0
18174 210.243 110.267 N 0
18187 222.781 83.4477 N 0
18202 266.502 37.2561 N 0
18218 260.299 112.772 N 0
18226 291.141 111.777 N 0
18238 204.171 35.3373 N 0
18264 202.881 111.66 N 0
18272 271.716 29.3876 N 0
18278 273.289 120.763 N 0
18289 192.211 4.24388 N 0
18306 123.481 32.1886 N 0
18315 144.778 11.8163 N 0
18321 158.357 87.5934 N 0
18334 207.454 77.2124 N 0
18342 213.869 102.258 N 0
18346 141.788 42.6336 N 0
18369 255.781 39.0551 N 0
18401 221.153 7.4296 N 0
18417 281.048 144.651 N 0
18441 131.034 81.5541 N 0
18474 174.717 101.471 N 0
18491 234.665 83.6114 N 0
18511 281.583 55.051 N 0
18535 285.203 10.3101 N 0
18544 122.354 114.603 N 0
18588 178.532 67.2692 N 0
18607 199.798 38.6119 N 0
18623 163.818 2.58909 N 0
18635 259.784 111.568 N 0
18647 90.3572 41.9332 N 0
18665 283.687 53.8695 N 0
18675 229.874 108.188 N 0
18687 133.927 88.6039 N 0
18713 151.887 91.785 N 0
18738 273.409 105.13 N 0
18761 291.763 111.532 N 0
18773 159.672 47.6649 N 0
18778 247.201 11.3046 N 0
18786 245.705 54.1258 N 0
18810 182.196 24.4148 N 0
18817 204.534 61.0023 N 0
18837 177.641 56.0356 N 0
18855 101.929 39.1692 N 0
18899 68.3603 140.864 N 0
18908 243.231 49.984 N 0
18914 206.076 9.76155 N 0
18923 152.663 72.8447 N 0
18950 293.904 63.691 N 0
18974 198.56 109 N 0
18991 240.622 69.649 N 0
19002 253.869 9.38462 N 0
19009 248.289 110.712 N 0
19025 167.689 34.9193 N 0
19048 256.493 36.0374 N 0
19069 214.596 23.541 N 0
19094 245.101 29.4621 N 0
19137 195.041 53.0575 N 0
19140 242.265 23.6546 N 0
19159 126.83 114.259 N 0
19193 237.076 68.0317 N 0
19224 213.852 62.8326 N 0
19241 251.631 55.8106 N 0
19259 220.686 101.805 N 0
19302 220.145 54.5468 N 0
19344 276.614 39.951 N 0
19357 204.666 69.2074 N 0
19388 264.859 67.8047 N 0
19413 243.587 20.9278 N 0
19433 170.717 71.6308 N 0
19474 203.223 98.3009 N 0
19489 138.522 27.3048 N 0
19497 185.415 49.24 N 0
19500 209.693 112.391 N 0
19521 291.07 62.2123 N 0
19528 261.42 35.3658 N 0
19544 197.405 13.9894 N 0
19552 295.046 83.0625 N 0
19564 171.172 35.7241 N 0
19576 234.528 69.285 N 0
19590 274.4 18.9775 N 0
19600 192.118 68.5477 N 0
19614 274.42 30.6377 N 0
19634 289.719 10.0873 N 0
19643 180.148 28.3612 N 0
19668 165.06 76.525 N 0
19695 276.26 61.3041 N 0
19703 149.413 111.78 N 0
19737 284.55 107.056 N 0
19757 144.641 65.798 N 0
19766 123.752 93.6217 N 0
19779 167.273 17.7213 N 0
19786 264.869 108.614 N 0
19793 286.237 144.826 N 0
19803 239.346 112.228 N 0
19830 276.684 47.9957 N 0
19838 152.203 101.775 N 0
19872 189.16 43.8103 N 0
19877 137.679 117.257 N 0
19891 239.314 63.4282 N 0
19905 204.956 20.4283 N 0
19914 132.293 110.267 N 0
19946 154.169 58.0537 N 0
19954 178.924 58.488 N 0
19964 248.891 114.323 N 0
19983 195.247 0.921335 N 0
19990 291.219 101.583 N 0
20008 163.471 48.0851 N 0
20015 145.664 14.8228 N 0
20020 192.094 23.8369 N 0
20029 289.208 150.301 N 0
20033 86.7249 147.841 N 0
20037 263.933 51.9614 N 0
20051 243.601 150.414 N 0
20069 150.512 53.6515 N 0
20102 74.6075 136.047 N 0
20133 252.065 36.6229 N 0
20157 152.405 39.8544 N 0
20171 200.478 99.1674 N 0
20183 205.356 49.129 N 0
20200 131.965 73.6945 N 0
20214 128.683 87.2541 N 0
20224 183.404 36.7942 N 0
20229 251.94 64.0439 N 0
20245 136.503 40.1655 N 0
20284 182.438 60.5181 N 0
20291 69.4255 138.073 N 0
20313 203.664 46.1529 N 0
20329 290.146 40.2365 N 0
20342 188.853 36.0832 N 0
20349 225.826 84.9731 N 0
20359 146.353 85.8275 N 0
20373 258.214 74.2176 N 0
20384 247.977 76.8299 N 0
20406 133.305 121.717 N 0
20413 245.609 145.192 N 0
20429 256.583 51.7282 N 0
20432 223.897 46.3398 N 0
20486 214.496 44.2761 N 0
20501 275.577 37.3446 N 0
20509 256.069 93.4378 N 0
20543 189.957 28.8672 N 0
20550 177.64 35.491 N 0
20595 248.942 34.2187 N 0
20629 125.882 75.6845 N 0
20642 149.054 33.6913 N 0
20666 248.999 51.5788 N 0
20685 234.068 105.066 N 0
20711 230.917 111.274 N 0
20734 209.581 10.6205 N 0
20752 220.122 7.95501 N 0
20768 229.191 6.65669 N 0
20781 158.495 37.0742 N 0
20793 129.363 73.6963 N 0
20813 277.885 27.0031 N 0
20828 127.129 82.7439 N 0
20863 252.615 111.653 N 0
20872 229.68 127.411 N 0
20895 222.225 111.575 N 0
20907 233.667 73.7105 N 0
20913 250.854 38.9823 N 0
20944 286.253 39.7279 N 0
20960 239.857 43.5393 N 0
20989 244.229 60.7131 N 0
21015 293.143 12.204 N 0
21026 56.2014 119.979 N 0
21053 288.978 98.4382 N 0
21097 172.849 4.92822 N 0
21110 263.892 49.4022 N 0
21125 187.372 14.7433 N 0
21135 262.773 43.584 N 0
21166 256.224 80.3481 N 0
21186 261.947 19.9099 N 0
21221 268.147 28.417 N 0
21233 138.357 73.548 N 0
21247 258.762 19.6427 N 0
21277 270.024 17.4334 N 0
21289 265.64 19.6508 N 0
21325 288.92 6.87668 N 0
21336 251.558 63.0494 N 0
21342 258.895 74.0012 N 0
21346 206.829 67.0001 N 0
21358 91.4861 181.788 N 0
21364 276.136 62.9773 N 0
21388 293.277 98.5724 N 0
21393 125.305 87.1784 N 0
21409 199.613 112.159 N 0
21417 249.155 34.6339 N 0
21443 251.08 87.3454 N 0
21502 240.234 36.8663 N 0
21523 212.074 61.0016 N 0
21533 155.205 17.3852 N 0
21536 90.1728 43.972 N 0
21548 281.467 144.689 N 0
21562 219.164 120.417 N 0
21575 232.986 12.6248 N 0
21587 255.314 34.125 N 0
21608 160.321 89.2124 N 0
21618 217.276 31.0699 N 0
21642 235.654 125.761 N 0
21651 257.823 39.6966 N 0
21671 212.219 77.7244 N 0
21704 258.377 113.074 N 0
21717 264.934 3.62742 N 0
21729 286.621 71.7632 N 0
21736 251.411 52.9238 N 0
21739 150.935 92.567 N 0
21757 162.121 58.6039 N 0
21765 241.147 141.187 N 0
21781 203.927 83.6488 N 0
21806 125.462 32.6635 N 0
21816 261.91 26.5491 N 0
21838 281.002 41.8882 N 0
21855 282.683 104.251 N 0
21874 198.789 43.077 N 0
21896 274.923 10.1134 N 0
21910 204.592 36.9727 N 0
21924 145.693 94.3025 N 0
21938 185.636 28.5363 N 0
21950 190.029 108.929 N 0
21959 261.296 28.5632 N 0
21987 196.951 100.349 N 0
21997 191.896 43.9142 N 0
22000 96.6128 41.5601 N 0
22010 261.748 67.6897 N 0
22056 196.921 78.4681 N 0
22065 262.127 67.669 N 0
22116 232.028 40.8122 N 0
22148 240.927 134.823 N 0
22152 217.83 34.8185 N 0
22159 234.233 59.9731 N 0
22190 228.587 12.945 N 0
22204 138.659 86.9706 N 0
22218 238 98.415 N 0
22232 164.282 49.6589 N 0
22236 256.025 84.8993 N 0
22256 186.426 1.2395 N 0
22263 228.783 27.4075 N 0
22287 195.652 28.9539 N 0
22295 89.5977 179.385 N 0
22302 280.404 63.7932 N 0
22310 254.188 12.6065 N 0
22320 208.36 48.497 N 0
22337 238.153 51.8061 N 0
22374 214.523 50.0751 N 0
22410 240.265 125.592 N 0
22421 253.301 87.6417 N 0
22441 227.183 94.3702 N 0
22449 206.201 86.9335 N 0
22462 212.158 117.757 N 0
22488 278.857 104.848 N 0
22510 259.415 34.8512 N 0
22525 279.878 135.085 N 0
22529 262.694 52.1419 N 0
22541 152.649 45.4453 N 0
22545 140.247 12.1334 N 0
22552 237.76 5.57448 N 0
22566 205.687 17.1988 N 0
22578 259.341 74.9569 N 0
22589 234.712 69.768 N 0
22601 209.918 13.7825 N 0
22644 229.799 54.3667 N 0
22679 225.846 68.7683 N 0
22717 219.296 24.6614 N 0
22736 229.603 17.0104 N 0
22765 206.548 105.955 N 0
22776 191.974 32.7395 N 0
22783 196.263 22.4924 N 0
22790 235.535 28.039 N 0
22806 277.972 76.1455 N 0
22813 184.705 42.3134 N 0
22820 296.344 144.84 N 0
22823 279.131 118.213 N 0
22826 296.516 49.868 N 0
22834 268.856 96.938 N 0
22847 296.641 92.96 N 0
22850 318.715 249.084 N 0
22853 297.482 182.227 N 0
22856 318.437 234.329 N 0
22859 324.456 118.83 N 0
22866 295.723 187.498 N 0
22869 326.533 104.15 N 0
22874 320.693 232.228 N 0
22877 121.31 165.781 N 0
22882 90.0297 232.414 N 0
22888 119.633 160.844 N 0
22891 82.5139 136.085 N 0
22899 116.125 246.684 N 0
22905 119.376 223.409 N 0
22910 96.5216 180.072 N 0
22914 123.329 114.741 N 0
22918 103.336 29.585 N 0
22927 88.6625 102.433 N 0
22930 71.0571 129.881 N 0
22939 176.158 174.48 N 0
22943 136.473 187.766 N 0
22947 146.037 121.586 N 0
22951 163.124 102.552 N 0
22969 89.9456 227.782 N 0
22974 30.6494 250.623 N 0
22977 58.596 116.935 N 0
22980 96.9415 258.19 N 0
22983 96.3225 240.757 N 0
22989 93.4832 177.411 N 0
22996 133.887 141.324 N 0
22999 147.962 173.69 N 0
23005 150.114 168.215 N 0
23008 89.8595 245.847 N 0
23014 95.3706 226.716 N 0
23020 84.7615 135.143 N 0
23023 175.713 147.981 N 0
23038 90.251 227.317 N 0
23044 92.6258 206.565 N 0
23048 86.9235 136.916 N 0
23051 114.656 25.6419 N 0
23056 88.179 40.2285 N 0
23059 105.932 19.38 N 0
23062 180.68 209.969 N 0
23067 87.6945 136.193 N 0
23070 89.7034 234.545 N 0
23076 89.6985 241.474 N 0
23081 63.156 162.488 N 0
23084 29.1855 80.3765 N 0
23087 88.179 90.583 N 0
23090 63.2851 270.317 N 0
23096 63.156 163 N 0
23099 29.241 84.7997 N 0
23104 105.769 267.069 N 0
23110 96.057 180.454 N 0
23114 134.503 142.837 N 0
23117 67.9077 273.144 N 0
23122 67.9055 162.7 N 0
23125 29.1533 84.764 N 0
23129 95.0324 249.159 N 0
23135 93.4845 179.714 N 0
23138 88.179 101.586 N 0
23141 66.1393 173.219 N 0
23146 89.6925 197.701 N 0
23151 84.9045 134.94 N 0
23154 124.041 189.567 N 0
23157 45.4184 23.3463 N 0
23247 46.88 38.4 N 0
23250 88.4722 94.4615 N 0
23314 107.794 19.6369 N 0
23385 45.2506 19.4634 N 0
23391 108.974 20.7018 N 0
23553 115.669 20.1873 N 0
23668 87.7177 159.068 N 0
23684 135.471 3.35201 N 0
23751 206.26 67.3404 N 0
23764 270.379 55.56 N 0
23767 119.318 46.8987 N 0
23783 270.577 130.337 N 0
23817 236.034 210.162 N 0
23835 296.485 191.697 N 0
23871 230.574 56.006 N 0
23881 300.541 136.147 N 0
23885 195.883 48.4849 N 0
23899 128.618 19.605 N 0
23902 298.12 75.394 N 0
23906 47.0225 38.4 N 0
23909 59.166 58.32 N 0
23912 129.732 78.12 N 0
23915 160.955 107.364 N 0
23919 146.388 129.125 N 0
23924 163.561 89.64 N 0
23927 163.96 43.5245 N 0
23931 117.506 47.665 N 0
23935 130.919 19.38 N 0
23938 146.176 142.8 N 0
23941 133.34 76.377 N 0
23944 121.436 44.8935 N 0
23947 209.576 6.50564 N 0
23953 268.248 107.905 N 0
23956 278.75 58.2546 N 0
23960 119.563 96.7765 N 0
23964 118.502 83.863 N 0
23967 128.351 110.795 N 0
23970 145.676 53.5585 N 0
23975 224.951 42.48 N 0
23978 160.901 36.7882 N 0
23987 114.348 25.3964 N 0
23992 277.689 58.0185 N 0
23996 291.279 94.0554 N 0
24000 90.5423 42.9421 N 0
24009 102.264 44.6535 N 0
24012 267.733 127.926 N 0
24016 217.632 74.581 N 0
24019 279.595 154.596 N 0
24022 314.636 157.267 N 0
24026 151.546 153.734 N 0
24032 148.038 185.598 N 0
24036 146.495 143.961 N 0
24039 159.859 149.372 N 0
24071 295.946 1.3842 N 0
24076 257.81 137.116 N 0
24085 187.081 66.8735 N 0
24088 241.002 58.2995 N 0
24093 88.2659 95.9705 N 0
24107 118.448 104.946 N 0
24111 119.301 77.52 N 0
24114 145.414 188.681 N 0
24121 180.554 210.827 N 0
24133 202.191 96.3633 N 0
24147 201.591 60.2579 N 0
24185 233.684 55.8567 N 0
24189 220.882 74.2135 N 0
24192 131.15 75.345 N 0
24195 146.436 144.713 N 0
24202 117.506 44.8935 N 0
24205 127.115 21.525 N 0
24212 129.516 72.7865 N 0
24215 117.506 49.838 N 0
24218 265.801 101.157 N 0
24222 93.998 41.5885 N 0
24236 45.0067 36.797 N 0
24254 132.363 14.831 N 0
24258 326.228 100.533 N 0
24263 179.294 44.88 N 0
24266 57.3156 125.472 N 0
24282 239.364 59.1843 N 0
24287 251.753 120.377 N 0
24293 249.346 88.0488 N 0
24301 125.212 132.205 N 0
24308 146.991 139.834 N 0
24311 54.183 115.767 N 0
24314 194.461 90.975 N 0
24317 298.595 106.92 N 0
24320 28.6425 25.32 N 0
24323 117.58 70.3568 N 0
24328 259.46 116.074 N 0
24332 266.473 99.428 N 0
24335 28.6425 24.36 N 0
24338 28.9275 82.439 N 0
24342 107.262 29.1454 N 0
24378 324.28 115.867 N 0
24381 82.8657 139.832 N 0
24393 204.932 46.434 N 0
24396 234.142 41.7002 N 0
24400 192.374 67.2547 N 0
24411 235.237 54.4498 N 0
24431 198.781 92.9365 N 0
24438 146.889 68.0145 N 0
24441 178.804 132.572 N 0
24446 145.773 44.486 N 0
24449 88.2635 91.831 N 0
24452 263.348 114.398 N 0
24456 146.234 141.987 N 0
24459 205.746 92.4722 N 0
24468 273.918 20.0129 N 0
24474 152.333 106.92 N 0
24477 154.46 67.35 N 0
24480 240.975 55.5985 N 0
24483 192.722 61.3014 N 0
24488 210.002 55.069 N 0
24502 204.969 96.0307 N 0
24519 290.096 154.297 N 0
24525 175.389 71.16 N 0
24528 245.691 53.6037 N 0
24537 265.852 30.724 N 0
24540 226.692 92.1438 N 0
24548 164.298 82.9279 N 0
24564 193.214 97.2629 N 0
24570 167.796 92.344 N 0
24574 198.786 86.6745 N 0
24577 169.161 90.7355 N 0
24580 135.038 112.483 N 0
24583 119.4 59.4692 N 0
24587 153.013 140.673 N 0
24594 227.59 20.256 N 0
24597 182.889 110.235 N 0
24600 142.731 141.451 N 0
24603 245.293 84.3375 N 0
24607 203.452 105.693 N 0
24616 85.9173 172.848 N 0
24628 238.435 171.101 N 0
24631 279.173 151.615 N 0
24634 110.566 22.6615 N 0
24691 59.4225 78.6 N 0
24694 260.421 84.053 N 0
24697 260.264 27.609 N 0
24700 184.492 71.475 N 0
24703 266.883 59.08 N 0
24708 132.208 76.282 N 0
24711 157.572 109.996 N 0
24714 270.135 135.773 N 0
24717 275.553 58.4955 N 0
24720 205.386 53.3227 N 0
24723 252.936 108.656 N 0
24726 261.368 97.1449 N 0
24736 224.535 29.3109 N 0
24748 117.506 49.838 N 0
24751 231.855 94.7505 N 0
24754 117.506 49.029 N 0
24757 203.549 125.371 N 0
24772 204.616 70.8295 N 0
24783 208.389 94.0126 N 0
24797 88.2616 84.9088 N 0
24813 58.482 127.08 N 0
24816 122.515 70.4995 N 0
24819 28.6425 35.88 N 0
24822 228.863 96.815 N 0
24828 218.499 120.752 N 0
24831 234.505 48.4731 N 0
24867 90.2053 39.7381 N 0
24886 291.436 89.726 N 0
24889 149.954 168.728 N 0
24892 146.775 121.693 N 0
24895 241.516 77.26 N 0
24898 63.954 58.32 N 0
24901 164.878 109.596 N 0
24904 252.453 61.32 N 0
24907 115.189 66.7431 N 0
24913 189.163 60.713 N 0
24916 216.915 92.4641 N 0
24927 227.94 110.435 N 0
24934 232.116 57.5212 N 0
24962 217.052 131.334 N 0
CodeElements/Plc_client/test/ariane_overlap_MACRO/netlist.pb.txt 0 → 100644
# proto-file: tensorflow/core/framework/graph.proto
# proto-message: tensorflow.GraphDef
node {
name: "i_ariane/i_frontend/i_icache/sram_block_1__tag_sram/mem/mem_inst_mem_256x45_256x16_0x0"
attr {
key: "height"
value {
f: 19.26
}
}
attr {
key: "orientation"
value {
placeholder: "N"
}
}
attr {
key: "type"
value {
placeholder: "MACRO"
}
}
attr {
key: "width"
value {
f: 29.355
}
}
attr {
key: "x"
value {
f: 341.9145
}
}
attr {
key: "y"
value {
f: 25.804
}
}
}
node {
name: "i_ariane/i_frontend/i_icache/sram_block_3__tag_sram/mem/mem_inst_mem_256x45_256x16_0x0"
attr {
key: "height"
value {
f: 19.26
}
}
attr {
key: "orientation"
value {
placeholder: "N"
}
}
attr {
key: "type"
value {
placeholder: "MACRO"
}
}
attr {
key: "width"
value {
f: 29.355
}
}
attr {
key: "x"
value {
f: 341.75
}
}
attr {
key: "y"
value {
f: 8.8835
}
}
}
\ No newline at end of file
CodeElements/Plc_client/test/sample_clustered_nomacro/netlist.pb.txt
......@@ -3,7 +3,6 @@
node {
name: "P0"
input: "Grp_2/Pinput"
input: "P1"
attr {
key: "side"
value {
......
CodeElements/Plc_client/test/sample_clustered_uniform_one_hard/initial.plc 0 → 100644
# Placement file for Circuit Training
# Source input file(s) : circuit_training/environment/test_data/sample_clustered/netlist.pb.txt
# This file : circuit_training/environment/test_data/sample_clustered/initial.plc
# Date : 2022-03-13 09:30:00
# Columns : 2 Rows : 2
# Width : 600.000 Height : 600.000
# Area : 17603.53279986302
# Wirelength : 0.0
# Wirelength cost : 0.0
# Congestion cost : 0.0
# Density cost : 0.2305
# Project : circuit_training
# Block : sample_clustered
# Routes per micron, hor : 70.33 ver : 74.51
# Routes used by macros, hor : 51.79 ver : 51.79
# Smoothing factor : 2
# Overlap threshold : 0.004
#
#
#
# Counts of node types:
# HARD_MACROs : 2
# HARD_MACRO_PINs : 4
# MACROs : 3
# MACRO_PINs : 6
# PORTs : 2
# SOFT_MACROs : 1
# SOFT_MACRO_PINs : 2
# STDCELLs : 0
#
# node_index x y orientation fixed
0 0 100 - 1
1 499 499 - 1
2 125 375 N 0
3 375 375 N 0
8 170 310 N 0
CodeElements/Plc_client/test/sample_clustered_uniform_one_hard/netlist.pb.txt 0 → 100644
# proto-file: tensorflow/core/framework/graph.proto
# proto-message: tensorflow.GraphDef
node {
name: "Grp_2"
attr {
key: "height"
value {
f: 10
}
}
attr {
key: "type"
value {
placeholder: "MACRO"
}
}
attr {
key: "width"
value {
f: 10
}
}
attr {
key: "x"
value {
f: 10
}
}
attr {
key: "y"
value {
f: 10
}
}
}
\ No newline at end of file
CodeElements/Plc_client/test/sample_clustered_uniform_one_soft/initial.plc 0 → 100644
# Placement file for Circuit Training
# Source input file(s) : circuit_training/environment/test_data/sample_clustered/netlist.pb.txt
# This file : circuit_training/environment/test_data/sample_clustered/initial.plc
# Date : 2022-03-13 09:30:00
# Columns : 2 Rows : 2
# Width : 600.000 Height : 600.000
# Area : 17603.53279986302
# Wirelength : 0.0
# Wirelength cost : 0.0
# Congestion cost : 0.0
# Density cost : 0.2305
# Project : circuit_training
# Block : sample_clustered
# Routes per micron, hor : 70.33 ver : 74.51
# Routes used by macros, hor : 51.79 ver : 51.79
# Smoothing factor : 2
# Overlap threshold : 0.004
#
#
#
# Counts of node types:
# HARD_MACROs : 2
# HARD_MACRO_PINs : 4
# MACROs : 3
# MACRO_PINs : 6
# PORTs : 2
# SOFT_MACROs : 1
# SOFT_MACRO_PINs : 2
# STDCELLs : 0
#
# node_index x y orientation fixed
0 0 100 - 1
1 499 499 - 1
2 125 375 N 0
3 375 375 N 0
8 170 310 N 0
CodeElements/Plc_client/test/sample_clustered_uniform_one_soft/netlist.pb.txt 0 → 100644
# proto-file: tensorflow/core/framework/graph.proto
# proto-message: tensorflow.GraphDef
node {
name: "Grp_2"
attr {
key: "height"
value {
f: 8
}
}
attr {
key: "type"
value {
placeholder: "macro"
}
}
attr {
key: "width"
value {
f: 8
}
}
attr {
key: "x"
value {
f: 0
}
}
attr {
key: "y"
value {
f: 0
}
}
}
node {
name: "Grp_2/Poutput_single_0"
attr {
key: "macro_name"
value {
placeholder: "Grp_2"
}
}
attr {
key: "type"
value {
placeholder: "macro_pin"
}
}
attr {
key: "x"
value {
f: 20
}
}
attr {
key: "x_offset"
value {
f: 0
}
}
attr {
key: "y"
value {
f: 45
}
}
attr {
key: "y_offset"
value {
f: 0
}
}
}
node {
name: "Grp_2/Pinput"
attr {
key: "macro_name"
value {
placeholder: "Grp_2"
}
}
attr {
key: "type"
value {
placeholder: "macro_pin"
}
}
attr {
key: "x"
value {
f: 20
}
}
attr {
key: "x_offset"
value {
f: 0
}
}
attr {
key: "y"
value {
f: 45
}
}
attr {
key: "y_offset"
value {
f: 0
}
}
}
CodeElements/Plc_client/test/sample_clustered_uniform_two_soft/initial.plc 0 → 100644
# Placement file for Circuit Training
# Source input file(s) : circuit_training/environment/test_data/sample_clustered/netlist.pb.txt
# This file : circuit_training/environment/test_data/sample_clustered/initial.plc
# Date : 2022-03-13 09:30:00
# Columns : 2 Rows : 2
# Width : 600.000 Height : 600.000
# Area : 17603.53279986302
# Wirelength : 0.0
# Wirelength cost : 0.0
# Congestion cost : 0.0
# Density cost : 0.2305
# Project : circuit_training
# Block : sample_clustered
# Routes per micron, hor : 70.33 ver : 74.51
# Routes used by macros, hor : 51.79 ver : 51.79
# Smoothing factor : 2
# Overlap threshold : 0.004
#
#
#
# Counts of node types:
# HARD_MACROs : 2
# HARD_MACRO_PINs : 4
# MACROs : 3
# MACRO_PINs : 6
# PORTs : 2
# SOFT_MACROs : 1
# SOFT_MACRO_PINs : 2
# STDCELLs : 0
#
# node_index x y orientation fixed
0 0 100 - 1
1 499 499 - 1
2 125 375 N 0
3 375 375 N 0
8 170 310 N 0
CodeElements/Plc_client/test/sample_clustered_uniform_two_soft/netlist.pb.txt 0 → 100644
# proto-file: tensorflow/core/framework/graph.proto
# proto-message: tensorflow.GraphDef
node {
name: "Grp_2"
attr {
key: "height"
value {
f: 8
}
}
attr {
key: "type"
value {
placeholder: "macro"
}
}
attr {
key: "width"
value {
f: 8
}
}
attr {
key: "x"
value {
f: 4
}
}
attr {
key: "y"
value {
f: 4
}
}
}
node {
name: "Grp_3"
attr {
key: "height"
value {
f: 8
}
}
attr {
key: "type"
value {
placeholder: "macro"
}
}
attr {
key: "width"
value {
f: 8
}
}
attr {
key: "x"
value {
f: 20
}
}
attr {
key: "y"
value {
f: 20
}
}
}
node {
name: "Grp_2/Poutput_single_0"
input: "Grp_3/Pinput"
attr {
key: "macro_name"
value {
placeholder: "Grp_2"
}
}
attr {
key: "type"
value {
placeholder: "macro_pin"
}
}
attr {
key: "x"
value {
f: 4
}
}
attr {
key: "x_offset"
value {
f: 0
}
}
attr {
key: "y"
value {
f: 4
}
}
attr {
key: "y_offset"
value {
f: 0
}
}
}
node {
name: "Grp_2/Pinput"
attr {
key: "macro_name"
value {
placeholder: "Grp_2"
}
}
attr {
key: "type"
value {
placeholder: "macro_pin"
}
}
attr {
key: "x"
value {
f: 4
}
}
attr {
key: "x_offset"
value {
f: 0
}
}
attr {
key: "y"
value {
f: 4
}
}
attr {
key: "y_offset"
value {
f: 0
}
}
}
node {
name: "Grp_3/Poutput_single_0"
input: "Grp_2/Pinput"
attr {
key: "macro_name"
value {
placeholder: "Grp_3"
}
}
attr {
key: "type"
value {
placeholder: "macro_pin"
}
}
attr {
key: "x"
value {
f: 20
}
}
attr {
key: "x_offset"
value {
f: 0
}
}
attr {
key: "y"
value {
f: 20
}
}
attr {
key: "y_offset"
value {
f: 0
}
}
}
node {
name: "Grp_3/Pinput"
attr {
key: "macro_name"
value {
placeholder: "Grp_3"
}
}
attr {
key: "type"
value {
placeholder: "macro_pin"
}
}
attr {
key: "x"
value {
f: 20
}
}
attr {
key: "x_offset"
value {
f: 0
}
}
attr {
key: "y"
value {
f: 20
}
}
attr {
key: "y_offset"
value {
f: 0
}
}
}
\ No newline at end of file
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