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

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Showing with 337 additions and 673 deletions
CodeElements/Grouping/README.md
...@@ -22,6 +22,13 @@ The following cartoon was recently provided by a Google engineer to explain the ...@@ -22,6 +22,13 @@ The following cartoon was recently provided by a Google engineer to explain the
## **How Groups Are Used** ## **How Groups Are Used**
Each group is recorded in the “.fix file” that is part of the input to the hMETIS hypergraph partitioner when the gate-level netlist is clustered into soft macros. Each group is recorded in the “.fix file” that is part of the input to the hMETIS hypergraph partitioner when the gate-level netlist is clustered into soft macros.
## **How Grouping Scripts Are used**
We provide [(an example)](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/CodeElements/Grouping/test/test.py) about the usage of our grouping scripts.
Basically our grouping scripts take follows as inputs: (i) [(setup_file)](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/CodeElements/Grouping/test/setup.tcl)
including enablement information (lefs/libs), synthesized gate-level netlist (*.v), def file with placed IOs (*.def); (ii) n_rows and n_cols determined by the [(Gridding)](https://github.com/TILOS-AI-Institute/MacroPlacement/tree/main/CodeElements/Gridding) step; (iii) K_in and K_out parameters; (iv) global_net_threshold for ignoring global nets. If a net has more than global_net_threshold instances, we ignore such net when we search "transitive" fanins and fanouts. After
running grouping scripts, you will get two files *.fix and *.fix.old. The ".fix.old" file contains the IOs or macros in the corresponding group while *.fix file only contains standard cells in each group.
# Thanks # Thanks
We thank Google engineers for Q&A in a shared document, as well as live discussions on May 19, 2022, that explained the grouping method used in Circuit Training. All errors of understanding and implementation are the authors'. We will rectify such errors as soon as possible after being made aware of them. We thank Google engineers for Q&A in a shared document, as well as live discussions on May 19, 2022, that explained the grouping method used in Circuit Training. All errors of understanding and implementation are the authors'. We will rectify such errors as soon as possible after being made aware of them.
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CodeElements/Grouping/ariane.fix deleted 100644 → 0
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CodeElements/Grouping/grouping.py deleted 100644 → 0
import os
import argparse
import time
import shutil
import sys
from math import floor
sys.path.append('./utils')
####################################################################################
#### Extract hypergraph, instance, IO ports and outline from netlist
####################################################################################
def GenerateHypergraph(openroad_exe, setup_file, extract_hypergraph_file):
temp_file = os.getcwd() + "/extract_hypergraph.tcl"
cmd = "cp " + setup_file + " " + temp_file
os.system(cmd)
with open(extract_hypergraph_file) as f:
content = f.read().splitlines()
f.close()
f = open(temp_file, "a")
f.write("\n")
for line in content:
f.write(line + "\n")
f.close()
cmd = openroad_exe + " " + temp_file
os.system(cmd)
cmd = "rm " + temp_file
os.system(cmd)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("design", help = "design name", type = str)
parser.add_argument("n_rows", help = "number of rows", type = int)
parser.add_argument("n_cols", help = "number of cols", type = int)
parser.add_argument("--setup_file", help = "setup file for openroad (default = setup.tcl)", type = str, default = "setup.tcl")
args = parser.parse_args()
design = args.design
n_rows = args.n_rows
n_cols = args.n_cols
setup_file = args.setup_file
pwd = os.getcwd()
# Specify the location of hmetis exe and openroad exe
openroad_exe = pwd + "/utils/openroad"
extract_hypergraph_file = pwd + "/utils/extract_hypergraph.tcl"
# Generate Hypergraph file
rpt_dir = pwd + "/rtl_mp"
hypergraph_file = rpt_dir + "/" + design + ".hgr"
io_name_file = hypergraph_file + ".io"
instance_name_file = hypergraph_file + ".instance"
outline_file = hypergraph_file + ".outline"
GenerateHypergraph(openroad_exe, setup_file, extract_hypergraph_file)
# Generate fix file
fix_file = design + ".fix"
print("[INFO] Generated fix file : ", fix_file)
# Step 1: Grouping bundled IOs based on n_rows and n_cols
with open(outline_file) as f:
content = f.read().splitlines()
f.close()
items = content[0].split()
floorplan_lx = float(items[0])
floorplan_ly = float(items[1])
floorplan_ux = float(items[2])
floorplan_uy = float(items[3])
width = (floorplan_ux - floorplan_lx) / n_cols
height = (floorplan_uy - floorplan_ly) / n_rows
group_map = { }
io_inst_map = { }
vertex_list = []
grid_row_max = 100000000
with open(io_name_file) as f:
content = f.read().splitlines()
f.close()
for line in content:
items = line.split()
io_name = items[0]
vertex_list.append(io_name)
x = (float(items[1]) + float(items[3])) / 2.0
y = (float(items[2]) + float(items[4])) / 2.0
hash_id = floor(y / height) * grid_row_max + floor(x / width)
io_inst_map[io_name] = hash_id
if hash_id not in group_map:
group_map[hash_id] = [io_name]
else:
group_map[hash_id].append(io_name)
hash_id = max(group_map.keys()) + 1
with open(instance_name_file) as f:
content = f.read().splitlines()
f.close()
for line in content:
items = line.split()
inst_name = items[0]
vertex_list.append(inst_name)
if (int(items[1]) == 1):
group_map[hash_id] = [inst_name]
io_inst_map[inst_name] = hash_id
hash_id += 1
glue_inst = { }
with open(hypergraph_file) as f:
content = f.read().splitlines()
f.close()
for i in range(1, len(content)):
items = [vertex_list[int(item) -1] for item in content[i].split()]
if(len(items) > 100):
continue
group_list = []
flag = False
for item in items:
if item in io_inst_map:
flag = True
group_list.append(item)
if (flag == True):
for item in items:
if item not in group_list:
for inst in group_list:
group_map[io_inst_map[inst]].append(item)
if item not in glue_inst:
glue_inst[item] = [inst]
else:
glue_inst[item].append(inst)
for key, value in glue_inst.items():
if(len(value) > 1):
print(value)
group_id = 0
f = open(fix_file, "w")
for key, value in group_map.items():
line = "group_" + str(group_id) + ": "
group_id += 1
for io in value:
line += io + " "
line += "\n"
f.write(line)
f.close()
CodeElements/Grouping/rtl_mp/ariane.hgr deleted 100644 → 0
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CodeElements/Grouping/rtl_mp/ariane.hgr.instance deleted 100644 → 0
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CodeElements/Grouping/rtl_mp/ariane.hgr.io deleted 100644 → 0
clk_i 0 876.155 0.07 876.225
rst_ni 0 785.435 0.07 785.505
boot_addr_i[63] 0 1325.28 0.07 1325.34
boot_addr_i[62] 0 658.035 0.07 658.105
boot_addr_i[61] 0 1317.99 0.07 1318.06
boot_addr_i[60] 0 1167.07 0.07 1167.15
boot_addr_i[59] 0 1408.99 0.07 1409.06
boot_addr_i[58] 0 1041.64 0.07 1041.7
boot_addr_i[57] 0 705.355 0.07 705.425
boot_addr_i[56] 0 728.875 0.07 728.945
boot_addr_i[55] 0 1474.52 0.07 1474.58
boot_addr_i[54] 0 1370.92 0.07 1370.98
boot_addr_i[53] 0 825.475 0.07 825.545
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boot_addr_i[51] 0 1309.04 0.07 1309.1
boot_addr_i[50] 0 701.715 0.07 701.785
boot_addr_i[49] 0 945.315 0.07 945.385
boot_addr_i[48] 0 1272.64 0.07 1272.7
boot_addr_i[47] 0 1190.88 0.07 1190.94
boot_addr_i[46] 0 1121.71 0.07 1121.79
boot_addr_i[45] 0 1349.07 0.07 1349.15
boot_addr_i[44] 0 1103.52 0.07 1103.58
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boot_addr_i[41] 0 1478.16 0.07 1478.22
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boot_addr_i[39] 0 947.275 0.07 947.345
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boot_addr_i[37] 0 832.755 0.07 832.825
boot_addr_i[36] 0 1219.99 0.07 1220.06
boot_addr_i[35] 0 867.195 0.07 867.265
boot_addr_i[34] 0 609.035 0.07 609.105
boot_addr_i[33] 0 1079.99 0.07 1080.06
boot_addr_i[32] 0 990.675 0.07 990.745
boot_addr_i[31] 0 856.275 0.07 856.345
boot_addr_i[30] 0 768.915 0.07 768.985
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boot_addr_i[27] 0 1141.59 0.07 1141.67
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boot_addr_i[24] 0 1277.95 0.07 1278.03
boot_addr_i[23] 0 923.475 0.07 923.545
boot_addr_i[22] 0 1236.23 0.07 1236.31
boot_addr_i[21] 0 992.635 0.07 992.705
boot_addr_i[20] 0 1316.31 0.07 1316.39
boot_addr_i[19] 0 941.675 0.07 941.745
boot_addr_i[18] 0 807.275 0.07 807.345
boot_addr_i[17] 0 681.835 0.07 681.905
boot_addr_i[16] 0 854.595 0.07 854.665
boot_addr_i[15] 0 1312.68 0.07 1312.74
boot_addr_i[14] 0 1096.23 0.07 1096.31
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boot_addr_i[12] 0 741.755 0.07 741.825
boot_addr_i[11] 0 1407.04 0.07 1407.1
boot_addr_i[10] 0 1305.4 0.07 1305.46
boot_addr_i[9] 0 1203.47 0.07 1203.55
boot_addr_i[8] 0 1267.04 0.07 1267.1
boot_addr_i[7] 0 1489.07 0.07 1489.15
boot_addr_i[6] 0 1425.23 0.07 1425.31
boot_addr_i[5] 0 983.675 0.07 983.745
boot_addr_i[4] 0 1012.52 0.07 1012.59
boot_addr_i[3] 0 897.995 0.07 898.065
boot_addr_i[2] 0 1439.8 0.07 1439.86
boot_addr_i[1] 0 1136.28 0.07 1136.34
boot_addr_i[0] 0 974.435 0.07 974.505
hart_id_i[63] 0 1107.16 0.07 1107.22
hart_id_i[62] 0 616.315 0.07 616.385
hart_id_i[61] 0 1412.64 0.07 1412.7
hart_id_i[60] 0 1449.04 0.07 1449.1
hart_id_i[59] 0 743.435 0.07 743.505
hart_id_i[58] 0 1209.07 0.07 1209.15
hart_id_i[57] 0 978.075 0.07 978.145
hart_id_i[56] 0 1376.23 0.07 1376.31
hart_id_i[55] 0 887.075 0.07 887.145
hart_id_i[54] 0 938.035 0.07 938.105
hart_id_i[53] 0 1483.47 0.07 1483.55
hart_id_i[52] 0 1452.68 0.07 1452.74
hart_id_i[51] 0 667.275 0.07 667.345
hart_id_i[50] 0 881.755 0.07 881.825
hart_id_i[49] 0 698.075 0.07 698.145
hart_id_i[48] 0 1319.95 0.07 1320.03
hart_id_i[47] 0 1499.71 0.07 1499.79
hart_id_i[46] 0 1347.11 0.07 1347.19
hart_id_i[45] 0 659.995 0.07 660.065
hart_id_i[44] 0 1345.44 0.07 1345.51
hart_id_i[43] 0 980.035 0.07 980.105
hart_id_i[42] 0 1245.19 0.07 1245.27
hart_id_i[41] 0 987.035 0.07 987.105
hart_id_i[40] 0 1061.8 0.07 1061.86
hart_id_i[39] 0 1139.92 0.07 1139.98
hart_id_i[38] 0 1481.8 0.07 1481.86
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hart_id_i[36] 0 1065.44 0.07 1065.51
hart_id_i[35] 0 994.315 0.07 994.385
hart_id_i[34] 0 1170.71 0.07 1170.79
hart_id_i[33] 0 1385.19 0.07 1385.27
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hart_id_i[31] 0 1261.71 0.07 1261.79
hart_id_i[30] 0 703.675 0.07 703.745
hart_id_i[29] 0 1310.71 0.07 1310.79
hart_id_i[28] 0 1130.68 0.07 1130.74
hart_id_i[27] 0 1427.19 0.07 1427.27
hart_id_i[26] 0 865.235 0.07 865.305
hart_id_i[25] 0 1001.59 0.07 1001.66
hart_id_i[24] 0 1339.83 0.07 1339.91
hart_id_i[23] 0 1192.56 0.07 1192.62
hart_id_i[22] 0 1088.95 0.07 1089.03
hart_id_i[21] 0 714.595 0.07 714.665
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hart_id_i[19] 0 1463.59 0.07 1463.67
hart_id_i[18] 0 819.875 0.07 819.945
hart_id_i[17] 0 1019.79 0.07 1019.86
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hart_id_i[13] 0 623.595 0.07 623.665
hart_id_i[12] 0 1490.76 0.07 1490.82
hart_id_i[11] 0 759.955 0.07 760.025
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hart_id_i[9] 0 805.315 0.07 805.385
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hart_id_i[7] 0 970.795 0.07 970.865
hart_id_i[6] 0 1363.64 0.07 1363.7
hart_id_i[5] 0 1098.19 0.07 1098.27
hart_id_i[4] 0 787.115 0.07 787.185
hart_id_i[3] 0 1494.4 0.07 1494.46
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hart_id_i[1] 0 1128.99 0.07 1129.06
hart_id_i[0] 0 750.715 0.07 750.785
irq_i[1] 0 649.075 0.07 649.145
irq_i[0] 0 1336.19 0.07 1336.27
ipi_i 0 847.315 0.07 847.385
time_irq_i 0 1163.44 0.07 1163.51
debug_req_i 0 672.595 0.07 672.665
axi_req_o[277] 0 689.115 0.07 689.185
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axi_req_o[275] 0 1177.99 0.07 1178.06
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axi_req_o[154] 0 1417.95 0.07 1418.03
axi_req_o[153] 0 1169.04 0.07 1169.1
axi_req_o[152] 0 778.155 0.07 778.225
axi_req_o[151] 0 716.275 0.07 716.345
axi_req_o[150] 0 739.795 0.07 739.865
axi_req_o[149] 0 617.995 0.07 618.065
axi_req_o[148] 0 1470.88 0.07 1470.94
axi_req_o[147] 0 1287.19 0.07 1287.27
axi_req_o[146] 0 661.675 0.07 661.745
axi_req_o[145] 0 692.755 0.07 692.825
axi_req_o[144] 0 1238.19 0.07 1238.27
axi_req_o[143] 0 1252.47 0.07 1252.55
axi_req_o[142] 0 1239.88 0.07 1239.94
axi_req_o[141] 0 1361.68 0.07 1361.74
axi_req_o[140] 0 1034.35 0.07 1034.43
axi_req_o[139] 0 1327.23 0.07 1327.31
axi_req_o[138] 0 601.755 0.07 601.825
axi_req_o[137] 0 818.195 0.07 818.265
axi_req_o[136] 0 1354.4 0.07 1354.46
axi_req_o[135] 0 1465.28 0.07 1465.34
axi_req_o[134] 0 1438.11 0.07 1438.19
axi_req_o[133] 0 670.915 0.07 670.985
axi_req_o[132] 0 738.115 0.07 738.185
axi_req_o[131] 0 1183.59 0.07 1183.67
axi_req_o[130] 0 830.795 0.07 830.865
axi_req_o[129] 0 1032.68 0.07 1032.74
axi_req_o[128] 0 870.835 0.07 870.905
axi_req_o[127] 0 812.595 0.07 812.665
axi_req_o[126] 0 1285.23 0.07 1285.31
axi_req_o[125] 0 816.235 0.07 816.305
axi_req_o[124] 0 892.675 0.07 892.745
axi_req_o[123] 0 850.955 0.07 851.025
axi_req_o[122] 0 848.995 0.07 849.065
axi_req_o[121] 0 1181.64 0.07 1181.7
axi_req_o[120] 0 1230.92 0.07 1230.98
axi_req_o[119] 0 1268.99 0.07 1269.06
axi_req_o[118] 0 930.755 0.07 930.825
axi_req_o[117] 0 752.675 0.07 752.745
axi_req_o[116] 0 712.635 0.07 712.705
axi_req_o[115] 0 792.715 0.07 792.785
axi_req_o[114] 0 665.315 0.07 665.385
axi_req_o[113] 0 1187.23 0.07 1187.31
axi_req_o[112] 0 996.275 0.07 996.345
axi_req_o[111] 0 1101.83 0.07 1101.91
axi_req_o[110] 0 883.435 0.07 883.505
axi_req_o[109] 0 612.675 0.07 612.745
axi_req_o[108] 0 1274.31 0.07 1274.39
axi_req_o[107] 0 654.395 0.07 654.465
axi_req_o[106] 0 1025.4 0.07 1025.46
axi_req_o[105] 0 1174.35 0.07 1174.43
axi_req_o[104] 0 1216.35 0.07 1216.43
axi_req_o[103] 0 861.595 0.07 861.665
axi_req_o[102] 0 1381.56 0.07 1381.62
axi_req_o[101] 0 965.475 0.07 965.545
axi_req_o[100] 0 988.995 0.07 989.065
axi_req_o[99] 0 679.875 0.07 679.945
axi_req_o[98] 0 1199.83 0.07 1199.91
axi_req_o[97] 0 1356.35 0.07 1356.43
axi_req_o[96] 0 916.195 0.07 916.265
axi_req_o[95] 0 1419.92 0.07 1419.98
axi_req_o[94] 0 727.195 0.07 727.265
axi_req_o[93] 0 874.475 0.07 874.545
axi_req_o[92] 0 1194.52 0.07 1194.58
axi_req_o[91] 0 829.115 0.07 829.185
axi_req_o[90] 0 801.675 0.07 801.745
axi_req_o[89] 0 718.235 0.07 718.305
axi_req_o[88] 0 1081.68 0.07 1081.74
axi_req_o[87] 0 1281.59 0.07 1281.67
axi_req_o[86] 0 1116.11 0.07 1116.19
axi_req_o[85] 0 1396.11 0.07 1396.19
axi_req_o[84] 0 621.635 0.07 621.705
axi_req_o[83] 0 1303.44 0.07 1303.51
axi_req_o[82] 0 798.035 0.07 798.105
axi_req_o[81] 0 1087.28 0.07 1087.34
axi_req_o[80] 0 614.355 0.07 614.425
axi_req_o[79] 0 1127.04 0.07 1127.1
axi_req_o[78] 0 901.635 0.07 901.705
axi_req_o[77] 0 700.035 0.07 700.105
axi_req_o[76] 0 1338.16 0.07 1338.22
axi_req_o[75] 0 1014.47 0.07 1014.54
axi_req_o[74] 0 749.035 0.07 749.105
axi_req_o[73] 0 1450.71 0.07 1450.79
axi_req_o[72] 0 1377.92 0.07 1377.98
axi_req_o[71] 0 821.835 0.07 821.905
axi_req_o[70] 0 721.875 0.07 721.945
axi_req_o[69] 0 650.755 0.07 650.825
axi_req_o[68] 0 838.075 0.07 838.145
axi_req_o[67] 0 656.355 0.07 656.425
axi_req_o[66] 0 896.315 0.07 896.385
axi_req_o[65] 0 779.835 0.07 779.905
axi_req_o[64] 0 863.555 0.07 863.625
axi_req_o[63] 0 981.715 0.07 981.785
axi_req_o[62] 0 927.115 0.07 927.185
axi_req_o[61] 0 1270.68 0.07 1270.74
axi_req_o[60] 0 1321.64 0.07 1321.7
axi_req_o[59] 0 841.715 0.07 841.785
axi_req_o[58] 0 628.915 0.07 628.985
axi_req_o[57] 0 1048.92 0.07 1048.98
axi_req_o[56] 0 1388.83 0.07 1388.91
axi_req_o[55] 0 1359.99 0.07 1360.06
axi_req_o[54] 0 1399.76 0.07 1399.82
axi_req_o[53] 0 952.595 0.07 952.665
axi_req_o[52] 0 1430.83 0.07 1430.91
axi_req_o[51] 0 890.715 0.07 890.785
axi_req_o[50] 0 1410.68 0.07 1410.74
axi_req_o[49] 0 1276.28 0.07 1276.34
axi_req_o[48] 0 1218.04 0.07 1218.1
axi_req_o[47] 0 696.395 0.07 696.465
axi_req_o[46] 0 1045.28 0.07 1045.34
axi_req_o[45] 0 754.355 0.07 754.425
axi_req_o[44] 0 1118.07 0.07 1118.15
axi_req_o[43] 0 1401.71 0.07 1401.79
axi_req_o[42] 0 1125.35 0.07 1125.43
axi_req_o[41] 0 1405.35 0.07 1405.43
axi_req_o[40] 0 1403.4 0.07 1403.46
axi_req_o[39] 0 1056.19 0.07 1056.27
axi_req_o[38] 0 925.435 0.07 925.505
axi_req_o[37] 0 1387.16 0.07 1387.22
axi_req_o[36] 0 1445.4 0.07 1445.46
axi_req_o[35] 0 1394.44 0.07 1394.51
axi_req_o[34] 0 1037.99 0.07 1038.06
axi_req_o[33] 0 958.195 0.07 958.265
axi_req_o[32] 0 912.555 0.07 912.625
axi_req_o[31] 0 625.275 0.07 625.345
axi_req_o[30] 0 810.915 0.07 810.985
axi_req_o[29] 0 1350.76 0.07 1350.82
axi_req_o[28] 0 668.955 0.07 669.025
axi_req_o[27] 0 734.475 0.07 734.545
axi_req_o[26] 0 1332.56 0.07 1332.62
axi_req_o[25] 0 1421.59 0.07 1421.67
axi_req_o[24] 0 1472.56 0.07 1472.62
axi_req_o[23] 0 747.075 0.07 747.145
axi_req_o[22] 0 643.475 0.07 643.545
axi_req_o[21] 0 1241.83 0.07 1241.91
axi_req_o[20] 0 1156.16 0.07 1156.22
axi_req_o[19] 0 605.395 0.07 605.465
axi_req_o[18] 0 663.635 0.07 663.705
axi_req_o[17] 0 1330.88 0.07 1330.94
axi_req_o[16] 0 1459.95 0.07 1460.03
axi_req_o[15] 0 1414.31 0.07 1414.39
axi_req_o[14] 0 690.795 0.07 690.865
axi_req_o[13] 0 1292.52 0.07 1292.58
axi_req_o[12] 0 976.395 0.07 976.465
axi_req_o[11] 0 950.915 0.07 950.985
axi_req_o[10] 0 794.395 0.07 794.465
axi_req_o[9] 0 1023.43 0.07 1023.51
axi_req_o[8] 0 647.115 0.07 647.185
axi_req_o[7] 0 774.515 0.07 774.585
axi_req_o[6] 0 687.155 0.07 687.225
axi_req_o[5] 0 1294.47 0.07 1294.55
axi_req_o[4] 0 1290.83 0.07 1290.91
axi_req_o[3] 0 1232.59 0.07 1232.67
axi_req_o[2] 0 907.235 0.07 907.305
axi_req_o[1] 0 772.555 0.07 772.625
axi_req_o[0] 0 1476.19 0.07 1476.27
axi_resp_i[81] 0 719.915 0.07 719.985
axi_resp_i[80] 0 859.915 0.07 859.985
axi_resp_i[79] 0 694.435 0.07 694.505
axi_resp_i[78] 0 1247.16 0.07 1247.22
axi_resp_i[77] 0 1372.59 0.07 1372.67
axi_resp_i[76] 0 834.435 0.07 834.505
axi_resp_i[75] 0 1498.04 0.07 1498.1
axi_resp_i[74] 0 1492.71 0.07 1492.79
axi_resp_i[73] 0 745.395 0.07 745.465
axi_resp_i[72] 0 1374.28 0.07 1374.34
axi_resp_i[71] 0 1054.52 0.07 1054.58
axi_resp_i[70] 0 1052.56 0.07 1052.62
axi_resp_i[69] 0 1007.2 0.07 1007.27
axi_resp_i[68] 0 710.955 0.07 711.025
axi_resp_i[67] 0 1123.4 0.07 1123.46
axi_resp_i[66] 0 1454.35 0.07 1454.43
axi_resp_i[65] 0 929.075 0.07 929.145
axi_resp_i[64] 0 1279.92 0.07 1279.98
axi_resp_i[63] 0 1099.88 0.07 1099.94
axi_resp_i[62] 0 1428.88 0.07 1428.94
axi_resp_i[61] 0 827.155 0.07 827.225
axi_resp_i[60] 0 845.355 0.07 845.425
axi_resp_i[59] 0 879.795 0.07 879.865
axi_resp_i[58] 0 1228.95 0.07 1229.03
axi_resp_i[57] 0 1301.76 0.07 1301.82
axi_resp_i[56] 0 1443.44 0.07 1443.51
axi_resp_i[55] 0 707.315 0.07 707.385
axi_resp_i[54] 0 885.395 0.07 885.465
axi_resp_i[53] 0 823.515 0.07 823.585
axi_resp_i[52] 0 776.195 0.07 776.265
axi_resp_i[51] 0 761.635 0.07 761.705
axi_resp_i[50] 0 1109.11 0.07 1109.19
axi_resp_i[49] 0 1154.47 0.07 1154.55
axi_resp_i[48] 0 725.515 0.07 725.585
axi_resp_i[47] 0 1341.8 0.07 1341.86
axi_resp_i[46] 0 894.355 0.07 894.425
axi_resp_i[45] 0 1176.31 0.07 1176.39
axi_resp_i[44] 0 603.435 0.07 603.505
axi_resp_i[43] 0 905.275 0.07 905.345
axi_resp_i[42] 0 1432.52 0.07 1432.58
axi_resp_i[41] 0 723.555 0.07 723.625
axi_resp_i[40] 0 1094.56 0.07 1094.62
axi_resp_i[39] 0 1070.76 0.07 1070.82
axi_resp_i[38] 0 932.715 0.07 932.785
axi_resp_i[37] 0 1185.28 0.07 1185.34
axi_resp_i[36] 0 1368.95 0.07 1369.03
axi_resp_i[35] 0 936.355 0.07 936.425
axi_resp_i[34] 0 1358.04 0.07 1358.1
axi_resp_i[33] 0 790.755 0.07 790.825
axi_resp_i[32] 0 781.795 0.07 781.865
axi_resp_i[31] 0 1212.71 0.07 1212.79
axi_resp_i[30] 0 1110.8 0.07 1110.86
axi_resp_i[29] 0 783.475 0.07 783.545
axi_resp_i[28] 0 1487.11 0.07 1487.19
axi_resp_i[27] 0 1263.4 0.07 1263.46
axi_resp_i[26] 0 1441.76 0.07 1441.82
axi_resp_i[25] 0 796.355 0.07 796.425
axi_resp_i[24] 0 638.155 0.07 638.225
axi_resp_i[23] 0 1021.76 0.07 1021.83
axi_resp_i[22] 0 1161.76 0.07 1161.82
axi_resp_i[21] 0 1248.83 0.07 1248.91
axi_resp_i[20] 0 1334.52 0.07 1334.58
axi_resp_i[19] 0 1234.56 0.07 1234.62
axi_resp_i[18] 0 757.995 0.07 758.065
axi_resp_i[17] 0 683.515 0.07 683.585
axi_resp_i[16] 0 889.035 0.07 889.105
axi_resp_i[15] 0 756.315 0.07 756.385
axi_resp_i[14] 0 1188.92 0.07 1188.98
axi_resp_i[13] 0 1288.88 0.07 1288.94
axi_resp_i[12] 0 1223.64 0.07 1223.7
axi_resp_i[11] 0 1114.44 0.07 1114.51
axi_resp_i[10] 0 1398.07 0.07 1398.15
axi_resp_i[9] 0 1392.47 0.07 1392.55
axi_resp_i[8] 0 639.835 0.07 639.905
axi_resp_i[7] 0 1227.28 0.07 1227.34
axi_resp_i[6] 0 1043.59 0.07 1043.67
axi_resp_i[5] 0 985.355 0.07 985.425
axi_resp_i[4] 0 1072.71 0.07 1072.79
axi_resp_i[3] 0 878.115 0.07 878.185
axi_resp_i[2] 0 732.515 0.07 732.585
axi_resp_i[1] 0 730.835 0.07 730.905
axi_resp_i[0] 0 678.195 0.07 678.265
CodeElements/Grouping/rtl_mp/ariane.hgr.weight deleted 100644 → 0
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CodeElements/Grouping/src/grouping.py 0 → 100644
import os
import argparse
import time
import shutil
import sys
from math import floor
####################################################################################
#### Extract hypergraph, instance, IO ports and outline from netlist
####################################################################################
def GenerateHypergraph(openroad_exe, setup_file, extract_hypergraph_file):
temp_file = os.getcwd() + "/extract_hypergraph.tcl"
cmd = "cp " + setup_file + " " + temp_file
os.system(cmd)
with open(extract_hypergraph_file) as f:
content = f.read().splitlines()
f.close()
f = open(temp_file, "a")
f.write("\n")
for line in content:
f.write(line + "\n")
f.close()
cmd = openroad_exe + " " + temp_file
os.system(cmd)
cmd = "rm " + temp_file
os.system(cmd)
####################################################################################
#### TraverseFanout in BFS manner
####################################################################################
def TraverseFanout(vertices_list, hyperedges_list, vertex_hash, seed_id, root_id, K_out):
# update the root vertex
vertex_hash[root_id] = [seed_id, 0]
visited = []
wavefront = []
wavefront.append(root_id)
while (len(wavefront) > 0):
vertex_id = wavefront.pop(0)
visited.append(vertex_id)
level_id = vertex_hash[vertex_id][1] + 1
if (level_id <= K_out):
for hyperedge_id in vertices_list[vertex_id]:
hyperedge = hyperedges_list[hyperedge_id]
# This hyperedge is driven by current edge
if (hyperedge[0] == vertex_id):
for i in range(1, len(hyperedge)):
if hyperedge[i] in visited:
pass
else:
if (vertex_hash[hyperedge[i]][1] > level_id):
vertex_hash[hyperedge[i]] = [seed_id, level_id]
wavefront.append(hyperedge[i])
elif (vertex_hash[hyperedge[i]][1] == level_id and vertex_hash[hyperedge[i]][1] > seed_id):
vertex_hash[hyperedge[i]] = [seed_id, level_id]
wavefront.append(hyperedge[i])
####################################################################################
#### TraverseFanin in BFS manner
####################################################################################
def TraverseFanin(vertices_list, hyperedges_list, vertex_hash, seed_id, root_id, K_in):
# update the root vertex
vertex_hash[root_id] = [seed_id, 0]
visited = []
wavefront = []
wavefront.append(root_id)
while (len(wavefront) > 0):
vertex_id = wavefront.pop(0)
visited.append(vertex_id)
level_id = vertex_hash[vertex_id][1] + 1
if (level_id <= K_in):
for hyperedge_id in vertices_list[vertex_id]:
hyperedge = hyperedges_list[hyperedge_id]
# This hyperedge is not driven by vertex_id
if (hyperedge[0] != vertex_id):
driver_id = hyperedge[0]
if driver_id in visited:
pass
else:
if (vertex_hash[driver_id][1] > level_id):
vertex_hash[driver_id] = [seed_id, level_id]
wavefront.append(driver_id)
elif (vertex_hash[driver_id][1] == level_id and vertex_hash[driver_id][1] > seed_id):
vertex_hash[driver_id] = [seed_id, level_id]
wavefront.append(driver_id)
def Grouping(design, n_rows, n_cols, K_in, K_out, setup_file, global_net_threshold, src_dir):
pwd = os.getcwd()
# Specify the location of hmetis exe and openroad exe
openroad_exe = src_dir + "/utils/openroad"
extract_hypergraph_file = src_dir + "/utils/extract_hypergraph.tcl"
# Generate Hypergraph file
rpt_dir = pwd + "/rtl_mp"
hypergraph_file = rpt_dir + "/" + design + ".hgr"
io_name_file = hypergraph_file + ".io"
instance_name_file = hypergraph_file + ".instance"
outline_file = hypergraph_file + ".outline"
fix_file = pwd + "/" + design + ".fix"
original_fix_file = pwd + "/" + design + ".fix.old"
GenerateHypergraph(openroad_exe, setup_file, extract_hypergraph_file)
# read hypergraph
# Each vertex corresponds to the hyperedge_id incident to it
# Each hyperedge corresponds to the vertices in it
with open (hypergraph_file) as f:
content = f.read().splitlines()
f.close()
items = content[0].split()
num_hyperedges = int(items[0])
num_vertices = int(items[1])
vertices_list = [[] for i in range(num_vertices)]
hyperedges_list = []
hyperedge_id = 0
for i in range(num_hyperedges):
items = content[i+1].split()
# ignore all the global nets
if (len(items) > global_net_threshold):
pass
hyperedge = [int(item) - 1 for item in items]
hyperedges_list.append(hyperedge)
for vertex in hyperedge:
vertices_list[vertex].append(hyperedge_id)
hyperedge_id += 1
# sort all the macros and IOs based on lexicographically smallest macro name
# get all the IOs and macros as seeds
seed_list = []
vertex_map = { }
vertex_name_list = []
# read all the IOs
io_pos_map = { }
with open(io_name_file) as f:
content = f.read().splitlines()
f.close()
vertex_id = 0
for line in content:
items = line.split()
io_name = items[0]
x = (float(items[1]) + float(items[3])) / 2.0
y = (float(items[2]) + float(items[4])) / 2.0
io_pos_map[io_name] = (x, y)
seed_list.append(io_name)
vertex_map[io_name] = vertex_id
vertex_name_list.append(io_name)
vertex_id += 1
# read all the instances (including macros)
with open(instance_name_file) as f:
content = f.read().splitlines()
f.close()
for line in content:
items = line.split()
inst_name = items[0]
vertex_map[inst_name] = vertex_id
vertex_name_list.append(inst_name)
vertex_id += 1
if (int(items[1]) == 1):
seed_list.append(inst_name)
# sort the seed
seed_list.sort()
# grouping all the directly K_in fanins and K_out fanouts respect to
# ios and instances.
vertex_hash = [[len(seed_list), max(K_in, K_out) + 1] for i in range(len(vertex_map))]
for i in range(len(seed_list)):
TraverseFanout(vertices_list, hyperedges_list, vertex_hash, i, vertex_map[seed_list[i]], K_out)
TraverseFanin(vertices_list, hyperedges_list, vertex_hash, i, vertex_map[seed_list[i]], K_in)
# group IOs to bundled pins based on their position
with open(outline_file) as f:
content = f.read().splitlines()
f.close()
items = content[0].split()
floorplan_lx = float(items[0])
floorplan_ly = float(items[1])
floorplan_ux = float(items[2])
floorplan_uy = float(items[3])
# get the vertical distance and horizontal distance
width = (floorplan_ux - floorplan_lx) / n_cols
height = (floorplan_uy - floorplan_ly) / n_rows
initial_group_map = { }
grid_row_max = 100000000
for io_name, pos in io_pos_map.items():
hash_id = floor( pos[1] / height) * grid_row_max + floor( pos[0] / width)
if hash_id not in initial_group_map:
initial_group_map[hash_id] = [io_name]
else:
initial_group_map[hash_id].append(io_name)
# final group list
group_list = [ ]
for hash_id, ios in initial_group_map.items():
group_list.append(ios)
for seed in seed_list:
if seed not in io_pos_map:
group_list.append([seed])
group_id_map = { }
for i in range(len(group_list)):
for vertex in group_list[i]:
group_id_map[vertex] = i
for i in range(len(vertex_hash)):
seed_id = vertex_hash[i][0]
if (seed_id < len(seed_list)):
vertex_name = vertex_name_list[i]
seed_name = seed_list[seed_id]
if vertex_name not in group_list[group_id_map[seed_name]]:
group_list[group_id_map[seed_name]].append(vertex_name)
f = open(original_fix_file, "w")
for group in group_list:
line = ""
for vertex in group:
line += vertex + ","
line = line[0:-1]
f.write(line + '\n')
f.close()
f = open(fix_file, "w")
for group in group_list:
line = ""
for vertex in group:
if vertex not in seed_list:
line += vertex + ","
if (len(line) > 0):
line = line[0:-1]
f.write(line + '\n')
f.close()
shutil.rmtree(rpt_dir)
CodeElements/Grouping/test/ariane.fix 0 → 100644
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CodeElements/Grouping/test/ariane.fix.old 0 → 100644
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CodeElements/Grouping/test/test.py 0 → 100644
import os
import sys
import argparse
# Load the src files
sys.path.append('../src')
from grouping import Grouping
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--design", help = "design name", type = str, default = "ariane")
parser.add_argument("--n_rows", help = "number of rows", type = int, default = "128")
parser.add_argument("--n_cols", help = "number of cols", type = int, default = "128")
parser.add_argument("--K_in", help = "K_in", type = int, default = "1")
parser.add_argument("--K_out", help = "K_out", type = int, default = "1")
parser.add_argument("--setup_file", help = "setup file for openroad (default = setup.tcl)", type = str, default = "setup.tcl")
parser.add_argument("--global_net_threshold", help = "global net threshold", type = int, default = 100)
args = parser.parse_args()
design = args.design
n_rows = args.n_rows
n_cols = args.n_cols
K_in = args.K_in
K_out = args.K_out
setup_file = args.setup_file
global_net_threshold = args.global_net_threshold
# To use the grouping function, you need to specify the directory of src file
src_dir = "../src"
Grouping(design, n_rows, n_cols, K_in, K_out, setup_file, global_net_threshold, src_dir)
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