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macroplacement
Commit b87955ca by sakundu
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Merge branch 'main' of github.com:TILOS-AI-Institute/MacroPlacement into main

parents b6210de9 dbd074f1
Showing with 3056 additions and 27 deletions
CodeElements/FDPlacement/FD.py
......@@ -610,7 +610,9 @@ class PlcObject:
elif (self.orientation == None):
self.str += "N "
else:
self.str += str(self.orientation) + " "
string = str(self.orientation).split('"')[0]
#self.str += str(self.orientation) + " "
self.str += string + " "
self.str += "0\n"
return self.str
......@@ -1054,22 +1056,23 @@ class PBFNetlist:
x_dir = 0
y_dir = 0
if (src_lx >= target_ux or src_ux <= target_lx or src_ly >= target_uy or src_uy <= target_ly):
# there is no overlap
return None, None
else:
src_cx = (src_lx + src_ux) / 2.0
src_cy = (src_ly + src_uy) / 2.0
target_cx = (target_lx + target_ux) / 2.0
target_cy = (target_ly + target_uy) / 2.0
x_dir = src_cx - target_cx
y_dir = src_cy - target_cy
# set the minimum value to 1e-12
min_dist = 1e-2
if (abs(x_dir) <= min_dist):
x_dir = -1 * min_dist
if (abs(y_dir) <= min_dist):
y_dir = -1 * min_dist
return x_dir, y_dir
if (src_cx == target_cx and src_cy == target_cy):
# fully overlap
x_dir = -1.0 / sqrt(2.0)
y_dir = -1.0 / sqrt(2.0)
return x_dir, y_dir
else:
x_dir = src_cx - target_cx
y_dir = src_cy - target_cy
dist = sqrt(x_dir * x_dir + y_dir * y_dir)
return x_dir / dist, y_dir / dist
# check the relative position
# This is for attractive force
......@@ -1092,11 +1095,11 @@ class PBFNetlist:
if (x_dir == None): # No overlap
f_r_x = 0.0
else:
f_r_x = repulsive_factor * 1.0 / x_dir
f_r_x = repulsive_factor * 1.0 * max_displacement * x_dir
if (y_dir == None): # No overlap
f_r_y = 0.0
else:
f_r_y = repulsive_factor * 1.0 / y_dir
f_r_y = repulsive_factor * 1.0 * max_displacement * y_dir
self.objects[src_macro].AddForce(f_r_x, f_r_y)
self.objects[target_macro].AddForce(-1 * f_r_x, -1 * f_r_y)
......@@ -1214,25 +1217,60 @@ class FDPlacer:
#self.plc.restore_placement(self.temp_plc_file)
self.plc.set_canvas_size(self.design.canvas_width, self.design.canvas_height)
self.PlotFromPlc(self.open_source_flag)
#self.PlotFromPlc(self.open_source_flag)
start_time = time.time()
self.FDPlacer(self.open_source_flag)
end_time = time.time()
self.final_netlist_pbf_file = self.run_dir + "/" + self.design_name + ".pb.txt.final"
self.final_plc_file = self.run_dir + "/" + self.design_name + ".plc.final"
self.final_plc_fig = self.run_dir + "/" + self.design_name + ".plc.final.png"
self.design.WriteNetlist(self.final_netlist_pbf_file, self.final_plc_file)
self.WritePlcFile(self.final_plc_file)
print("************************************************")
print("The results from Circuit Training")
self.CalCostPlc(self.final_plc_file, isPrint = True)
print("runtime : ", end_time - start_time)
print("\n")
self.PlotFromPlc(self.open_source_flag, self.final_plc_fig)
start_time = time.time()
self.FDPlacer(not self.open_source_flag)
end_time = time.time()
self.final_netlist_pbf_file = self.run_dir + "/" + self.design_name + ".os.pb.txt.final"
self.final_plc_file = self.run_dir + "/" + self.design_name + ".os.plc.final"
self.final_plc_fig = self.run_dir + "/" + self.design_name + ".os.plc.final.png"
self.design.WriteNetlist(self.final_netlist_pbf_file, self.final_plc_file)
print("************************************************")
print("The results from Our Implementation")
self.CalCostPlc(self.final_plc_file, isPrint = True)
print("runtime : ", end_time - start_time)
print("\n")
self.PlotFromPlc(not self.open_source_flag, self.final_plc_fig)
### Call the plc client for cost evulation
def CalCostPlc(self, plc_file, isPrint = True):
self.plc.restore_placement(plc_file)
self.plc.set_canvas_boundary_check(False)
self.plc.make_soft_macros_square()
self.plc.set_placement_grid(self.design.n_cols, self.design.n_rows)
self.plc.set_routes_per_micron(self.design.hroute_per_micro, self.design.vroute_per_micro)
self.plc.set_macro_routing_allocation(self.design.hrouting_alloc, self.design.vrouting_alloc)
self.plc.set_congestion_smooth_range(self.design.smooth_factor)
self.plc.set_overlap_threshold(self.design.overlap_threshold)
self.plc.set_canvas_size(self.design.canvas_width, self.design.canvas_height)
wl_cost = self.plc.get_cost()
den_cost = self.plc.get_density_cost()
cong_cost = self.plc.get_congestion_cost()
# the weight parameters are given by Circuit Training
proxy_cost = wl_cost + 0.5 * den_cost + 0.5 * cong_cost
if (isPrint == True):
print("WL cost : ", wl_cost, "Density cost : ", den_cost, "Congestion Cost : ", cong_cost, "Proxy cost : ", proxy_cost)
return proxy_cost
def PlotFromPlc(self, open_source_flag = True, figure_file = None):
plt.figure(constrained_layout= True, figsize=(8,5), dpi=600)
if (open_source_flag == True):
......@@ -1334,7 +1372,7 @@ if __name__ == "__main__":
io_factor = 0
num_steps = [1]
attract_factor = [0.0]
repel_factor = [1.0]
repel_factor = [10.0]
move_distance_factors = [0.1] # set the max_displacement to 50
use_current_loc = True
placer = FDPlacer(design_name, run_dir, netlist_file, plc_file, io_factor, num_steps, attract_factor, repel_factor, move_distance_factors, use_current_loc)
......
CodeElements/FDPlacement/README.md
......@@ -2,7 +2,7 @@
**Force-directed placement** is used to place center of standard cell clusters onto
the center of the grid cells.
## **Information provided by Google.**
## **Information provided by Google**
The Methods section of the [Nature paper](https://www.nature.com/articles/s41586-021-03544-w.epdf?sharing_token=tYaxh2mR5EozfsSL0WHZLdRgN0jAjWel9jnR3ZoTv0PW0K0NmVrRsFPaMa9Y5We9O4Hqf_liatg-lvhiVcYpHL_YQpqkurA31sxqtmA-E1yNUWVMMVSBxWSp7ZFFIWawYQYnEXoBE4esRDSWqubhDFWUPyI5wK_5B_YIO-D_kS8%3D) provides the following information.
* “(1) We group millions of standard cells into a few thousand clusters using hMETIS, a partitioning technique based
......@@ -17,11 +17,32 @@ according to the weight×distance formula, causing tightly connected nodes to be
We also introduce a repulsive force between overlapping nodes to reduce placement density.
After applying all forces, we move nodes in the direction of their force vector. To reduce oscillations, we set a maximum distance for each move.”
For each pair of nodes $(u, v)$, there are two types of forces between them : attractive force $f_a(u,v)$ and repulsive force $f_r(u,v)$. The magnitude of attractive force can be expressed as $f_a(u, v) = k_a * distance(u, v)$, where $k_a$ is the attractive factor and $distance$ is the euclidean distance between node $u$ and node $v$; the magnitude of repulsive force can be expressed as $f_r(u, v) = k_r$. Both attracive force and repulsive force are along the straight line joining the node $u$ and node $v$.
## **Our implementation**
Our force-directed placer takes a clustered netlist as input and generates the locations for standard-cell clusters. During the force-directed placement, all the hard macros and IO ports are fixed. Only the standard-cell clusters can be moved but the standard-cell clusters are not necessarily placed onto the centers of gridcells. At the beginning, all the standard-cell clusters will be placed at the center of the canvas. \[[code](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/5addfc904527d764ee67429811c868c5eeb605d4/CodeElements/FDPlacement/FD.py#L1130)\]
During force-directed placement, all the hard macros and IO ports are fixed. Only the stanard-cell clusters can be moved. Note that we need to consider the contribution from hard macros and IO ports when we calculate the total force exerted on a standard-cell cluster.
In the force-directed placement, there are two types of forces between nodes: attractive force and repulsive force.
* The attractive force is ONLY applied to the nodes connected by nets. For the two-pin net connecting pin $P1$ of macro $M1$ and $P2$ of macro $M2$, the attractive force applied to $M1$ and $M2$ is calculated as $f_x = k_a * abs(P1.x - P2.x)$ and $f_y = k_a * abs(P1.y - P2.y)$, where $k_a$ is the attractive factor. If one of pins is an IO port, $k_a$ is the attractive factor times io factor. The attractive force is along the straight line joining $P1$ and $P2$. All the multi-pin nets are decomposied into two-pin nets using the start model. \[[code](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/5addfc904527d764ee67429811c868c5eeb605d4/CodeElements/FDPlacement/FD.py#L1105)\]
* The repulsive force is ONLY applied to the nodes overlapped with each other. If two macros are not overlapped, there is no repulsive force between them. For the two macros $M1$ and $M2$ overlapped with each other, the repulsive force applied to $M1$ and $M2$ is calculated as $f_x = F * abs(M1.x - M2.x) / distance(M1, M2)$ and $f_y = F * abs(M1.y - M2.y) / distance(M1, M2)$, where $F$ is the maximum move distance specified by the users. The repulsive force is along the straight line joining $M1$ and $M2$. For the cases where multiple macros are overlapped together, we calculate the repulsive forces between each pair of macros in sequence. \[[code](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/5addfc904527d764ee67429811c868c5eeb605d4/CodeElements/FDPlacement/FD.py#L1082)\]
After calculating all the attractive forces and repulsive forces, all the forces are normalized as following:
* f_x = f_x / f_x_max * max_move_distance
* f_y = f_y / f_y_max * max_move_distance
Here f_x_max (f_y_max) is the absolute value of f_x (f_y) which has the maximum absolute value. max_move_distance is the maximum move distance specified by users. \[[code](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/5addfc904527d764ee67429811c868c5eeb605d4/CodeElements/FDPlacement/FD.py#L1137)\]
After normalization, the standard-cell clusters are moved based on the forces exerted on them. The move which will push the standard-cell clusters outside of canvas will be canceled.
## **Experimental results**
We have tested our codes on the Ariane133 (NanGate45). The experimental results are presented below.
<p align="center">
<img src="./ariane133/FD_result.png" width= "1200"/>
</p>
<p align="center">
Figure 1. results for Ariane133 (NanGate45).
</p>
The standard-cell clusters are not placed onto the centers of gridcells.
......
CodeElements/FDPlacement/ariane133/ariane.os.pb.txt.final 0 → 100644
This source diff could not be displayed because it is too large. You can view the blob instead.
CodeElements/FDPlacement/ariane133/ariane.os.plc.final 0 → 100644
# Placement file for Circuit Training
# Source input file(s) : ./output_ariane_NanGate45/23cols_28rows/g500_ub5_nruns10_c5_r3_v3_rc1/netlist.pb.txt
# This file : ./output_ariane_NanGate45/23cols_28rows/g500_ub5_nruns10_c5_r3_v3_rc1/initial.plc
# Date : 2022-09-20 11:30:06
# Columns : 23 Rows : 28
# Width : 1347.100 Height : 1346.800
# Area : 1506097.566312155
# Wirelength : 3610985.229
# Wirelength cost : 0.0526
# Congestion cost : 0.7721
# Density cost : 0.7209
# Project : circuit_training
# Block : unset_block
# Routes per micron, hor : 11.285 ver : 12.605
# Routes used by macros, hor : 7.143 ver : 8.339
# Smoothing factor : 2
# Overlap threshold : 0.004
#
#
#
# Counts of node types:
# HARD_MACROs : 133
# HARD_MACRO_PINs : 7847
# MACROs : 943
# MACRO_PINs : 19658
# PORTs : 495
# SOFT_MACROs : 810
# SOFT_MACRO_PINs : 11811
# STDCELLs : 0
#
# User comments:
# Original source netlist with standard cells: /home/zf4_projects/DREAMPlace/sakundu/ABK_MP/08162022_flow_scripts/MacroPlacement/Flows/NanGate45/ariane133/run-20220816-035413/flow2_1.3/ariane.pb.txt
# Groups file: ./output_ariane_NanGate45/23cols_28rows/g500_ub5_nruns10_c5_r3_v3_rc1/groups.final
# worst 10 spread
# grp: 744 - spread: 278916.7518893204
# grp: 50 - spread: 268009.6376835485
# grp: 336 - spread: 223800.4868365636
# grp: 535 - spread: 207184.36226282385
# grp: 505 - spread: 197871.97286953646
# grp: 586 - spread: 190713.73374938426
# grp: 756 - spread: 184033.9279079139
# grp: 532 - spread: 173094.29808680012
# grp: 773 - spread: 170707.79035165644
# grp: 347 - spread: 168578.0558490756
#
#
# node_index x y orientation fixed
0 0.035 232.47 - 0
1 0.035 235.13 - 0
2 0.035 237.79 - 0
3 0.035 240.45 - 0
4 0.035 243.11 - 0
5 0.035 245.77 - 0
6 0.035 248.43 - 0
7 0.035 251.23 - 0
8 0.035 253.89 - 0
9 0.035 256.55 - 0
10 0.035 259.21 - 0
11 0.035 261.87 - 0
12 0.035 264.53 - 0
13 0.035 267.19 - 0
14 0.035 269.99 - 0
15 0.035 272.65 - 0
16 0.035 275.31 - 0
17 0.035 277.97 - 0
18 0.035 280.63 - 0
19 0.035 283.29 - 0
20 0.035 285.95 - 0
21 0.035 288.75 - 0
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24 0.035 296.73 - 0
25 0.035 299.39 - 0
26 0.035 302.05 - 0
27 0.035 304.71 - 0
28 0.035 307.51 - 0
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30 0.035 312.83 - 0
31 0.035 315.49 - 0
32 0.035 318.15 - 0
33 0.035 320.81 - 0
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36 0.035 328.93 - 0
37 0.035 331.59 - 0
38 0.035 334.25 - 0
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41 0.035 342.23 - 0
42 0.035 345.03 - 0
43 0.035 347.69 - 0
44 0.035 350.35 - 0
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48 0.035 361.13 - 0
49 0.035 363.79 - 0
50 0.035 366.45 - 0
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52 0.035 371.77 - 0
53 0.035 374.43 - 0
54 0.035 377.09 - 0
55 0.035 379.89 - 0
56 0.035 382.55 - 0
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59 0.035 390.53 - 0
60 0.035 393.19 - 0
61 0.035 395.85 - 0
62 0.035 398.65 - 0
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66 0.035 409.29 - 0
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78 0.035 441.49 - 0
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100 0.035 500.43 - 0
101 0.035 503.09 - 0
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18002 362.966585376826 822.062008605268 N 0
18042 351.244070887523 821.56124495923 N 0
18051 369.21334300595 586.45779627948 N 0
18065 821.972570888114 714.289084587572 N 0
18093 364.412115248748 730.08315853847 N 0
18126 418.188450244453 403.575435971039 N 0
18138 649.32112478323 416.731312622301 N 0
18151 652.624233725181 744.403707353252 N 0
18174 845.400171367339 905.902785339872 N 0
18178 478.226484247879 852.757636345395 N 0
18181 548.036942745684 850.268236928666 N 0
18188 707.750422061131 595.435836520248 N 0
18193 510.401794024409 451.067799200814 N 0
18201 436.027662549456 667.479386135423 N 0
18213 808.648207073282 747.793002963211 N 0
18222 482.331618445525 886.078452635458 N 0
18234 602.019446872699 602.620322016848 N 0
18241 264.575995716156 550.942100233604 N 0
18255 353.059517787976 706.344070582613 N 0
18289 750.330542094873 746.203605663133 N 0
18294 245.826047013958 822.633378129902 N 0
18305 907.030032765591 621.935783805943 N 0
18336 542.49968377585 502.884014925775 N 0
18357 644.330114342582 813.38878292797 N 0
18400 365.968764380878 762.791355609554 N 0
18424 599.190789162189 742.406273727705 N 0
18453 572.674393823969 770.256349945061 N 0
18473 689.50544975993 878.319803661037 N 0
18483 625.954205274808 563.766408963662 N 0
18505 713.041164036244 610.908508429383 N 0
18529 664.023210484973 531.943307219948 N 0
18561 955.130946278644 577.26829845982 N 0
18567 177.136748292574 667.72226810901 N 0
18606 702.765452125253 465.451445449864 N 0
18614 781.610750976091 428.009086994197 N 0
18630 391.936019976164 826.574913711516 N 0
18659 707.807443005684 734.550150932328 N 0
18669 813.273838228802 559.47868777957 N 0
18732 426.969948366725 671.675752801525 N 0
18792 653.765279363443 601.403685901149 N 0
18800 713.879956386929 326.145090109024 N 0
18810 728.018165510342 450.595333800489 N 0
18821 462.398129835194 544.959830380344 N 0
18830 809.546490502281 772.690203801282 N 0
18835 796.312529073279 492.616732442659 N 0
18844 543.942007387569 602.381455802246 N 0
18881 798.989977169043 722.722205213347 N 0
18899 802.10456939348 468.443260111308 N 0
18913 485.198392703017 724.747520938764 N 0
18938 535.352023447265 929.27481497546 N 0
18961 379.673268791218 606.366299856773 N 0
18973 597.941576271726 764.759388984405 N 0
19012 280.681228159517 819.601951139839 N 0
19033 553.064163863858 885.110782659042 N 0
19041 641.8292563459 744.7803074492 N 0
19056 940.939659078097 903.3391978693 N 0
19070 308.84775538537 786.839509563857 N 0
19100 712.190853629397 781.393872905966 N 0
19112 729.73868716615 679.379974027748 N 0
19126 373.103307569945 639.253291165989 N 0
19138 968.105920963931 741.848653788004 N 0
19166 541.219205512528 462.245411193177 N 0
19181 357.854394032657 885.42371929288 N 0
19207 212.592274565226 575.806927512542 N 0
19216 762.67906706166 947.007210765404 N 0
19231 778.792238440456 749.441572830549 N 0
19237 329.542235724743 455.392414705962 N 0
19246 496.487693667913 563.168177030895 N 0
19253 737.217005662521 765.656583523235 N 0
19259 797.444200507416 753.067652526773 N 0
19266 351.76384855027 650.503665536261 N 0
19283 665.553907546834 777.38989669876 N 0
19298 484.045145291791 645.221986217911 N 0
19312 727.692207876876 751.423231257607 N 0
19322 786.783047692885 592.348882954025 N 0
19327 593.211690201871 610.154429943466 N 0
19361 495.059421381787 929.559372726284 N 0
19366 800.745065795756 764.824056205541 N 0
19370 521.243857758387 630.051704671232 N 0
19409 495.945599220965 697.152028825509 N 0
19430 601.342589247496 790.690659659955 N 0
19444 329.600951508608 542.298446169641 N 0
19456 779.277875739473 769.525699914844 N 0
19461 564.705050103391 733.564639253671 N 0
19481 428.430892451053 601.407978272607 N 0
19485 706.938675470051 635.133150291721 N 0
19492 608.282193123372 651.010277545746 N 0
19499 529.077262787445 959.803054827446 N 0
19522 575.394314673451 787.570578359526 N 0
19551 925.825043115373 627.611730066656 N 0
19562 761.051085763855 568.324254162369 N 0
19579 1006.603339117497 629.964119553897 N 0
19586 781.119072289812 736.854736707218 N 0
19623 333.108198299404 608.224390514801 N 0
19636 823.230780332991 427.362856300436 N 0
19641 568.348325459995 942.349502269922 N 0
19646 572.002991863363 527.445669331913 N 0
19661 515.439153504203 462.663674277989 N 0
19680 705.169086602821 730.530058037175 N 0
19695 237.705271893252 791.759055093636 N 0
19730 675.602877115984 740.651320761079 N 0
19749 749.951298389476 780.105785088771 N 0
19756 220.790957420172 639.825508153135 N 0
19767 513.710586170347 486.522748110585 N 0
19778 860.435132062818 793.539224915332 N 0
19811 359.250098890105 540.420794623373 N 0
19815 295.32646596179 667.812822592764 N 0
19839 627.326578886837 543.173414150438 N 0
19880 378.264046234684 356.394776829338 N 0
19888 339.004239424234 726.800441584967 N 0
19905 555.022681031291 605.038231246229 N 0
19919 740.471048805676 612.776402328952 N 0
19926 421.171046382223 756.487404743314 N 0
19941 667.460098524658 747.585468390602 N 0
19948 653.470136307077 916.139483563805 N 0
19961 346.98975564964 677.156378362658 N 0
19986 797.763767026787 447.369078608969 N 0
20020 918.776963006881 756.180588565961 N 0
20056 529.866960969353 755.698565643718 N 0
20109 634.525123238151 578.898499979516 N 0
20121 510.206990493368 740.866715071863 N 0
20137 240.292958141991 496.484198033286 N 0
20147 402.007317576215 728.741737793924 N 0
20169 663.503601485947 370.366033145155 N 0
20183 277.281367389503 376.856183997347 N 0
20194 361.719357426872 484.450414850386 N 0
20209 497.39228500738 708.102037175828 N 0
20250 529.903964224354 689.337998780011 N 0
20270 300.835750628349 483.013024777419 N 0
20282 757.729956829386 741.555226440958 N 0
20310 627.187425298746 319.978647926104 N 0
20326 688.67074439614 611.333961225282 N 0
20345 375.783308103134 786.700389855567 N 0
20362 527.005092549689 889.613107012958 N 0
20372 480.399801412856 736.001583328857 N 0
20393 238.574461288836 762.050668864717 N 0
20423 734.925627356197 851.166831228245 N 0
20430 880.578035255908 734.050124920658 N 0
20466 683.617058412518 572.833440974506 N 0
20482 658.020470275294 584.860943273735 N 0
20491 297.732516362128 454.463321098696 N 0
20509 387.616426216213 443.11345985527 N 0
20531 816.060640863415 524.518600547567 N 0
20543 433.215620251318 903.870747069376 N 0
20578 439.289405692787 501.394876615888 N 0
20590 809.433953662535 614.367949688118 N 0
20595 393.745544658454 666.902431270201 N 0
20606 1056.396110753319 913.247928715577 N 0
20633 1147.897705779034 1000.704368542919 N 0
20636 470.987818740599 1230.705773269453 N 0
20640 748.849438963699 452.825686986455 N 0
20644 797.425105712722 152.056129854919 N 0
20647 876.029055352205 1182.247170270823 N 0
20651 970.153846176426 766.396303104633 N 0
20658 191.370041099727 1194.572068426722 N 0
20661 205.04339132079 1122.21541948871 N 0
20664 583.020546582415 1201.884410702225 N 0
20667 637.824419808828 902.252881726054 N 0
20672 393.405093586294 1202.404684525986 N 0
20675 599.364939000531 986.65699872352 N 0
20678 771.871395539743 613.378429701735 N 0
20681 471.988542677598 1064.33402399752 N 0
20685 330.352378666462 811.424709286613 N 0
20688 600.327239370949 639.452855552216 N 0
20692 318.370001428096 399.06493410298 N 0
20696 264.527596843287 487.068456897386 N 0
20702 196.854280236578 695.982645233502 N 0
20725 747.375632403675 620.410092055742 N 0
20728 847.908623602042 469.732792845574 N 0
20739 792.829240759263 908.201849228958 N 0
20744 781.994047009084 457.229909490672 N 0
20752 216.042609688451 871.535352920582 N 0
20767 217.166858694974 865.384755100605 N 0
20787 267.321973313451 943.608164186868 N 0
20807 199.74472954268 976.36423854701 N 0
20827 592.815135067203 1050.647224242828 N 0
20831 935.539515699603 1040.204136146775 N 0
20837 218.422468228583 847.609369924964 N 0
20852 722.3912194698 1047.434342833857 N 0
20860 888.407016664036 889.31674540058 N 0
20863 1013.705381496833 618.760524671426 N 0
20866 447.048567418384 627.086574089874 N 0
20875 879.888492401386 597.724166604337 N 0
20880 689.203072434645 763.098110543673 N 0
20883 633.77075412644 586.453801353807 N 0
20886 305.279669894595 1042.339371122858 N 0
20900 692.917307496112 766.395799519541 N 0
20903 689.540541005146 758.968528840847 N 0
20906 223.069989444221 825.611519674601 N 0
20914 726.335883307619 596.994274683794 N 0
20917 487.54320559806 1049.455095990483 N 0
20943 369.172632037982 692.178128213611 N 0
20952 683.491599152061 749.3798626898 N 0
20959 358.70044755998 619.364273132206 N 0
20962 382.120145361429 918.542692996723 N 0
20970 376.248866806923 712.105610841419 N 0
20973 274.397411917016 707.133321753786 N 0
20977 239.97374289169 702.950898505297 N 0
20992 711.235882995414 910.879686563724 N 0
20995 620.328020286953 734.878547757502 N 0
20998 555.335554884906 749.923902671895 N 0
21001 868.377075686606 616.166314028783 N 0
21005 931.272503843147 590.02021586328 N 0
21028 933.432827701579 753.761529834028 N 0
21032 910.376863087154 604.065398858845 N 0
21053 939.719860053885 746.043708839121 N 0
21062 354.586262698596 624.469167114644 N 0
21065 684.784022844793 762.687729714138 N 0
21068 670.140182766625 600.304173354824 N 0
21071 853.844335539525 618.722100805713 N 0
21077 736.466538017179 720.062038148312 N 0
21080 672.529635299864 604.50237277584 N 0
21083 748.902816848153 469.097584527579 N 0
21086 587.627336116914 458.414045947564 N 0
21090 312.12420867123 590.954279779455 N 0
21093 357.710320923052 624.494937705686 N 0
CodeElements/FDPlacement/ariane133/ariane.pb.txt.final 0 → 100644
This source diff could not be displayed because it is too large. You can view the blob instead.
CodeElements/FDPlacement/ariane133/ariane.plc.final 0 → 100644
# Placement file for Circuit Training
# Source input file(s) : ./output_ariane_NanGate45/23cols_28rows/g500_ub5_nruns10_c5_r3_v3_rc1/netlist.pb.txt
# This file : ./output_ariane_NanGate45/23cols_28rows/g500_ub5_nruns10_c5_r3_v3_rc1/initial.plc
# Date : 2022-09-20 11:30:06
# Columns : 23 Rows : 28
# Width : 1347.100 Height : 1346.800
# Area : 1506097.566312155
# Wirelength : 3610985.229
# Wirelength cost : 0.0526
# Congestion cost : 0.7721
# Density cost : 0.7209
# Project : circuit_training
# Block : unset_block
# Routes per micron, hor : 11.285 ver : 12.605
# Routes used by macros, hor : 7.143 ver : 8.339
# Smoothing factor : 2
# Overlap threshold : 0.004
#
#
#
# Counts of node types:
# HARD_MACROs : 133
# HARD_MACRO_PINs : 7847
# MACROs : 943
# MACRO_PINs : 19658
# PORTs : 495
# SOFT_MACROs : 810
# SOFT_MACRO_PINs : 11811
# STDCELLs : 0
#
# User comments:
# Original source netlist with standard cells: /home/zf4_projects/DREAMPlace/sakundu/ABK_MP/08162022_flow_scripts/MacroPlacement/Flows/NanGate45/ariane133/run-20220816-035413/flow2_1.3/ariane.pb.txt
# Groups file: ./output_ariane_NanGate45/23cols_28rows/g500_ub5_nruns10_c5_r3_v3_rc1/groups.final
# worst 10 spread
# grp: 744 - spread: 278916.7518893204
# grp: 50 - spread: 268009.6376835485
# grp: 336 - spread: 223800.4868365636
# grp: 535 - spread: 207184.36226282385
# grp: 505 - spread: 197871.97286953646
# grp: 586 - spread: 190713.73374938426
# grp: 756 - spread: 184033.9279079139
# grp: 532 - spread: 173094.29808680012
# grp: 773 - spread: 170707.79035165644
# grp: 347 - spread: 168578.0558490756
#
#
# node_index x y orientation fixed
0 0.035 232.47 - 0
1 0.035 235.13 - 0
2 0.035 237.79 - 0
3 0.035 240.45 - 0
4 0.035 243.11 - 0
5 0.035 245.77 - 0
6 0.035 248.43 - 0
7 0.035 251.23 - 0
8 0.035 253.89 - 0
9 0.035 256.55 - 0
10 0.035 259.21 - 0
11 0.035 261.87 - 0
12 0.035 264.53 - 0
13 0.035 267.19 - 0
14 0.035 269.99 - 0
15 0.035 272.65 - 0
16 0.035 275.31 - 0
17 0.035 277.97 - 0
18 0.035 280.63 - 0
19 0.035 283.29 - 0
20 0.035 285.95 - 0
21 0.035 288.75 - 0
22 0.035 291.41 - 0
23 0.035 294.07 - 0
24 0.035 296.73 - 0
25 0.035 299.39 - 0
26 0.035 302.05 - 0
27 0.035 304.71 - 0
28 0.035 307.51 - 0
29 0.035 310.17 - 0
30 0.035 312.83 - 0
31 0.035 315.49 - 0
32 0.035 318.15 - 0
33 0.035 320.81 - 0
34 0.035 323.47 - 0
35 0.035 326.27 - 0
36 0.035 328.93 - 0
37 0.035 331.59 - 0
38 0.035 334.25 - 0
39 0.035 336.91 - 0
40 0.035 339.57 - 0
41 0.035 342.23 - 0
42 0.035 345.03 - 0
43 0.035 347.69 - 0
44 0.035 350.35 - 0
45 0.035 353.01 - 0
46 0.035 355.67 - 0
47 0.035 358.33 - 0
48 0.035 361.13 - 0
49 0.035 363.79 - 0
50 0.035 366.45 - 0
51 0.035 369.11 - 0
52 0.035 371.77 - 0
53 0.035 374.43 - 0
54 0.035 377.09 - 0
55 0.035 379.89 - 0
56 0.035 382.55 - 0
57 0.035 385.21 - 0
58 0.035 387.87 - 0
59 0.035 390.53 - 0
60 0.035 393.19 - 0
61 0.035 395.85 - 0
62 0.035 398.65 - 0
63 0.035 401.31 - 0
64 0.035 403.97 - 0
65 0.035 406.63 - 0
66 0.035 409.29 - 0
67 0.035 411.95 - 0
68 0.035 414.61 - 0
69 0.035 417.41 - 0
70 0.035 420.07 - 0
71 0.035 422.73 - 0
72 0.035 425.39 - 0
73 0.035 428.05 - 0
74 0.035 430.71 - 0
75 0.035 433.37 - 0
76 0.035 436.17 - 0
77 0.035 438.83 - 0
78 0.035 441.49 - 0
79 0.035 444.15 - 0
80 0.035 446.81 - 0
81 0.035 449.47 - 0
82 0.035 452.13 - 0
83 0.035 454.93 - 0
84 0.035 457.59 - 0
85 0.035 460.25 - 0
86 0.035 462.91 - 0
87 0.035 465.57 - 0
88 0.035 468.23 - 0
89 0.035 470.89 - 0
90 0.035 473.69 - 0
91 0.035 476.35 - 0
92 0.035 479.01 - 0
93 0.035 481.67 - 0
94 0.035 484.33 - 0
95 0.035 486.99 - 0
96 0.035 489.79 - 0
97 0.035 492.45 - 0
98 0.035 495.11 - 0
99 0.035 497.77 - 0
100 0.035 500.43 - 0
101 0.035 503.09 - 0
102 0.035 505.75 - 0
103 0.035 508.55 - 0
104 0.035 511.21 - 0
105 0.035 513.87 - 0
106 0.035 516.53 - 0
107 0.035 519.19 - 0
108 0.035 521.85 - 0
109 0.035 524.51 - 0
110 0.035 527.31 - 0
111 0.035 529.97 - 0
112 0.035 532.63 - 0
113 0.035 535.29 - 0
114 0.035 537.95 - 0
115 0.035 540.61 - 0
116 0.035 543.27 - 0
117 0.035 546.07 - 0
118 0.035 548.73 - 0
119 0.035 551.39 - 0
120 0.035 554.05 - 0
121 0.035 556.71 - 0
122 0.035 559.37 - 0
123 0.035 562.03 - 0
124 0.035 564.83 - 0
125 0.035 567.49 - 0
126 0.035 570.15 - 0
127 0.035 572.81 - 0
128 0.035 575.47 - 0
129 0.035 578.13 - 0
130 0.035 580.79 - 0
131 0.035 583.59 - 0
132 0.035 586.25 - 0
133 0.035 588.91 - 0
134 0.035 591.57 - 0
135 0.035 594.23 - 0
136 0.035 596.89 - 0
137 0.035 599.55 - 0
138 0.035 602.35 - 0
139 0.035 605.01 - 0
140 0.035 607.67 - 0
141 0.035 610.33 - 0
142 0.035 612.99 - 0
143 0.035 615.65 - 0
144 0.035 618.45 - 0
145 0.035 621.11 - 0
146 0.035 623.77 - 0
147 0.035 626.43 - 0
148 0.035 629.09 - 0
149 0.035 631.75 - 0
150 0.035 634.41 - 0
151 0.035 637.21 - 0
152 0.035 639.87 - 0
153 0.035 642.53 - 0
154 0.035 645.19 - 0
155 0.035 647.85 - 0
156 0.035 650.51 - 0
157 0.035 653.17 - 0
158 0.035 655.97 - 0
159 0.035 658.63 - 0
160 0.035 661.29 - 0
161 0.035 663.95 - 0
162 0.035 666.61 - 0
163 0.035 669.27 - 0
164 0.035 671.93 - 0
165 0.035 674.73 - 0
166 0.035 677.39 - 0
167 0.035 680.05 - 0
168 0.035 682.71 - 0
169 0.035 685.37 - 0
170 0.035 688.03 - 0
171 0.035 690.69 - 0
172 0.035 693.49 - 0
173 0.035 696.15 - 0
174 0.035 698.81 - 0
175 0.035 701.47 - 0
176 0.035 704.13 - 0
177 0.035 706.79 - 0
178 0.035 709.45 - 0
179 0.035 712.25 - 0
180 0.035 714.91 - 0
181 0.035 717.57 - 0
182 0.035 720.23 - 0
183 0.035 722.89 - 0
184 0.035 725.55 - 0
185 0.035 728.21 - 0
186 0.035 731.01 - 0
187 0.035 733.67 - 0
188 0.035 736.33 - 0
189 0.035 738.99 - 0
190 0.035 741.65 - 0
191 0.035 744.31 - 0
192 0.035 747.11 - 0
193 0.035 749.77 - 0
194 0.035 752.43 - 0
195 0.035 755.09 - 0
196 0.035 757.75 - 0
197 0.035 760.41 - 0
198 0.035 763.07 - 0
199 0.035 765.87 - 0
200 0.035 768.53 - 0
201 0.035 771.19 - 0
202 0.035 773.85 - 0
203 0.035 776.51 - 0
204 0.035 779.17 - 0
205 0.035 781.83 - 0
206 0.035 784.63 - 0
207 0.035 787.29 - 0
208 0.035 789.95 - 0
209 0.035 792.61 - 0
210 0.035 795.27 - 0
211 0.035 797.93 - 0
212 0.035 800.59 - 0
213 0.035 803.39 - 0
214 0.035 806.05 - 0
215 0.035 808.71 - 0
216 0.035 811.37 - 0
217 0.035 814.03 - 0
218 0.035 816.69 - 0
219 0.035 819.35 - 0
220 0.035 822.15 - 0
221 0.035 824.81 - 0
222 0.035 827.47 - 0
223 0.035 830.13 - 0
224 0.035 832.79 - 0
225 0.035 835.45 - 0
226 0.035 838.11 - 0
227 0.035 840.91 - 0
228 0.035 843.57 - 0
229 0.035 846.23 - 0
230 0.035 848.89 - 0
231 0.035 851.55 - 0
232 0.035 854.21 - 0
233 0.035 856.87 - 0
234 0.035 859.67 - 0
235 0.035 862.33 - 0
236 0.035 864.99 - 0
237 0.035 867.65 - 0
238 0.035 870.31 - 0
239 0.035 872.97 - 0
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CodeElements/FDPlacement/simple_example/simple.os.pb.txt.final
......@@ -60,13 +60,13 @@ node {
attr {
key: "x"
value {
f: 265.306122
f: 300.0
}
}
attr {
key: "y"
value {
f: 265.306122
f: 300.0
}
}
}
......
CodeElements/FDPlacement/simple_example/simple.os.plc.final
......@@ -45,7 +45,7 @@
#
#
# node_index x y orientation fixed
0 201.0 201.0 "N" 0
1 265.30612244898 265.30612244898 N 0
0 201.0 201.0 0
1 300.0 300.0 N 0
2 285.0 285.0 N 0
3 400.0 400.0 N 0
CodeElements/FDPlacement/simple_example/simple.plc.final
......@@ -46,6 +46,6 @@
#
# node_index x y orientation fixed
0 201 201 N 0
1 250 250 N 0
2 335 335 N 0
3 350 350 N 0
1 300 300 N 0
2 285 285 N 0
3 400 400 N 0
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