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macroplacement
Commit 83935698 by ZhiangWang033
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add testcase for groupping

parent d507cb8d
Showing with 662 additions and 673 deletions
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
This source diff could not be displayed because it is too large. You can view the blob instead.
CodeElements/Grouping/rtl_mp/ariane.hgr.instance deleted 100644 → 0
This source diff could not be displayed because it is too large. You can view the blob instead.
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
boot_addr_i[52] 0 1210.76 0.07 1210.82
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
boot_addr_i[43] 0 1090.92 0.07 1090.98
boot_addr_i[42] 0 763.595 0.07 763.665
boot_addr_i[41] 0 1478.16 0.07 1478.22
boot_addr_i[40] 0 610.715 0.07 610.785
boot_addr_i[39] 0 947.275 0.07 947.345
boot_addr_i[38] 0 1179.95 0.07 1180.03
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
boot_addr_i[29] 0 1105.47 0.07 1105.55
boot_addr_i[28] 0 1365.31 0.07 1365.39
boot_addr_i[27] 0 1141.59 0.07 1141.67
boot_addr_i[26] 0 1468.92 0.07 1468.98
boot_addr_i[25] 0 840.035 0.07 840.105
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
boot_addr_i[13] 0 641.795 0.07 641.865
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
hart_id_i[37] 0 1258.07 0.07 1258.15
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
hart_id_i[32] 0 1461.64 0.07 1461.7
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
hart_id_i[20] 0 1027.07 0.07 1027.15
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
hart_id_i[16] 0 963.515 0.07 963.585
hart_id_i[15] 0 1367.28 0.07 1367.34
hart_id_i[14] 0 1147.19 0.07 1147.27
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
hart_id_i[10] 0 767.235 0.07 767.305
hart_id_i[9] 0 805.315 0.07 805.385
hart_id_i[8] 0 969.115 0.07 969.185
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
hart_id_i[2] 0 1201.8 0.07 1201.86
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
axi_req_o[276] 0 1050.88 0.07 1050.94
axi_req_o[275] 0 1177.99 0.07 1178.06
axi_req_o[274] 0 1132.64 0.07 1132.7
axi_req_o[273] 0 1416.28 0.07 1416.34
axi_req_o[272] 0 959.875 0.07 959.945
axi_req_o[271] 0 934.395 0.07 934.465
axi_req_o[270] 0 814.555 0.07 814.625
axi_req_o[269] 0 652.715 0.07 652.785
axi_req_o[268] 0 1165.4 0.07 1165.46
axi_req_o[267] 0 1314.35 0.07 1314.43
axi_req_o[266] 0 1005.23 0.07 1005.3
axi_req_o[265] 0 899.955 0.07 900.025
axi_req_o[264] 0 1148.88 0.07 1148.94
axi_req_o[263] 0 765.275 0.07 765.345
axi_req_o[262] 0 1250.8 0.07 1250.86
axi_req_o[261] 0 1379.88 0.07 1379.94
axi_req_o[260] 0 1423.56 0.07 1423.62
axi_req_o[259] 0 954.555 0.07 954.625
axi_req_o[258] 0 1243.52 0.07 1243.58
axi_req_o[257] 0 1383.52 0.07 1383.58
axi_req_o[256] 0 1352.71 0.07 1352.79
axi_req_o[255] 0 627.235 0.07 627.305
axi_req_o[254] 0 1016.16 0.07 1016.22
axi_req_o[253] 0 1029.04 0.07 1029.1
axi_req_o[252] 0 1145.23 0.07 1145.31
axi_req_o[251] 0 607.075 0.07 607.145
axi_req_o[250] 0 1172.68 0.07 1172.74
axi_req_o[249] 0 1259.76 0.07 1259.82
axi_req_o[248] 0 1196.19 0.07 1196.27
axi_req_o[247] 0 1456.31 0.07 1456.39
axi_req_o[246] 0 1112.76 0.07 1112.82
axi_req_o[245] 0 1137.95 0.07 1138.03
axi_req_o[244] 0 1063.47 0.07 1063.55
axi_req_o[243] 0 1298.11 0.07 1298.19
axi_req_o[242] 0 1134.31 0.07 1134.39
axi_req_o[241] 0 1254.44 0.07 1254.51
axi_req_o[240] 0 1158.11 0.07 1158.19
axi_req_o[239] 0 910.875 0.07 910.945
axi_req_o[238] 0 956.235 0.07 956.305
axi_req_o[237] 0 1496.35 0.07 1496.43
axi_req_o[236] 0 770.875 0.07 770.945
axi_req_o[235] 0 1085.31 0.07 1085.39
axi_req_o[234] 0 943.635 0.07 943.705
axi_req_o[233] 0 1221.68 0.07 1221.74
axi_req_o[232] 0 1078.04 0.07 1078.1
axi_req_o[231] 0 1328.92 0.07 1328.98
axi_req_o[230] 0 676.235 0.07 676.305
axi_req_o[229] 0 1214.4 0.07 1214.46
axi_req_o[228] 0 1074.4 0.07 1074.46
axi_req_o[227] 0 872.515 0.07 872.585
axi_req_o[226] 0 1143.56 0.07 1143.62
axi_req_o[225] 0 1092.59 0.07 1092.67
axi_req_o[224] 0 1152.52 0.07 1152.58
axi_req_o[223] 0 836.395 0.07 836.465
axi_req_o[222] 0 1467.23 0.07 1467.31
axi_req_o[221] 0 972.755 0.07 972.825
axi_req_o[220] 0 1150.83 0.07 1150.91
axi_req_o[219] 0 1198.16 0.07 1198.22
axi_req_o[218] 0 1003.55 0.07 1003.62
axi_req_o[217] 0 1119.76 0.07 1119.82
axi_req_o[216] 0 852.635 0.07 852.705
axi_req_o[215] 0 1076.35 0.07 1076.43
axi_req_o[214] 0 1008.88 0.07 1008.95
axi_req_o[213] 0 961.835 0.07 961.905
axi_req_o[212] 0 1018.11 0.07 1018.18
axi_req_o[211] 0 1207.11 0.07 1207.19
axi_req_o[210] 0 997.955 0.07 998.025
axi_req_o[209] 0 1283.56 0.07 1283.62
axi_req_o[208] 0 921.795 0.07 921.865
axi_req_o[207] 0 1083.64 0.07 1083.7
axi_req_o[206] 0 674.555 0.07 674.625
axi_req_o[205] 0 1030.71 0.07 1030.79
axi_req_o[204] 0 803.635 0.07 803.705
axi_req_o[203] 0 685.475 0.07 685.545
axi_req_o[202] 0 1059.83 0.07 1059.91
axi_req_o[201] 0 1299.8 0.07 1299.86
axi_req_o[200] 0 630.875 0.07 630.945
axi_req_o[199] 0 1323.59 0.07 1323.67
axi_req_o[198] 0 868.875 0.07 868.945
axi_req_o[197] 0 903.595 0.07 903.665
axi_req_o[196] 0 1434.47 0.07 1434.55
axi_req_o[195] 0 1447.07 0.07 1447.15
axi_req_o[194] 0 1307.07 0.07 1307.15
axi_req_o[193] 0 857.955 0.07 858.025
axi_req_o[192] 0 808.955 0.07 809.025
axi_req_o[191] 0 1343.47 0.07 1343.55
axi_req_o[190] 0 1010.84 0.07 1010.91
axi_req_o[189] 0 919.835 0.07 919.905
axi_req_o[188] 0 1069.07 0.07 1069.15
axi_req_o[187] 0 619.955 0.07 620.025
axi_req_o[186] 0 789.075 0.07 789.145
axi_req_o[185] 0 1390.8 0.07 1390.86
axi_req_o[184] 0 908.915 0.07 908.985
axi_req_o[183] 0 918.155 0.07 918.225
axi_req_o[182] 0 1296.16 0.07 1296.22
axi_req_o[181] 0 708.995 0.07 709.065
axi_req_o[180] 0 1265.35 0.07 1265.43
axi_req_o[179] 0 1047.23 0.07 1047.31
axi_req_o[178] 0 1205.44 0.07 1205.51
axi_req_o[177] 0 636.195 0.07 636.265
axi_req_o[176] 0 1058.16 0.07 1058.22
axi_req_o[175] 0 1039.95 0.07 1040.03
axi_req_o[174] 0 1485.44 0.07 1485.51
axi_req_o[173] 0 645.435 0.07 645.505
axi_req_o[172] 0 799.995 0.07 800.065
axi_req_o[171] 0 939.995 0.07 940.065
axi_req_o[170] 0 1159.8 0.07 1159.86
axi_req_o[169] 0 948.955 0.07 949.025
axi_req_o[168] 0 1457.99 0.07 1458.06
axi_req_o[167] 0 736.155 0.07 736.225
axi_req_o[166] 0 1436.16 0.07 1436.22
axi_req_o[165] 0 634.515 0.07 634.585
axi_req_o[164] 0 914.515 0.07 914.585
axi_req_o[163] 0 1256.11 0.07 1256.19
axi_req_o[162] 0 1225.31 0.07 1225.39
axi_req_o[161] 0 999.915 0.07 999.985
axi_req_o[160] 0 1479.83 0.07 1479.91
axi_req_o[159] 0 1067.11 0.07 1067.19
axi_req_o[158] 0 632.555 0.07 632.625
axi_req_o[157] 0 1036.31 0.07 1036.39
axi_req_o[156] 0 843.675 0.07 843.745
axi_req_o[155] 0 967.155 0.07 967.225
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/src/grouping.py.old 0 → 100644
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)
####################################################################################
#### 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)
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
instance_list = []
K_in = args.K_in
K_out = args.K_out
setup_file = args.setup_file
global_net_threshold = args.global_net_threshold
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"
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/src/utils/extract_hypergraph.tcl 0 → 100644
#######################################################################
### Author: Zhiang, May, 2022
### Uses OpenROAD API to convert the netlist to a hypergraph (in hemtis format)
### This file cannot be used directly. It will be called by
### generate_cluster.py
### All the parameters set here are meaningless.
### !!! Please don't touch this file !!!
########################################################################
read_verilog $netlist
link_design $top_design
read_sdc $sdc
read_def $def_file -floorplan_initialize
#Generate the hypergraph
partition_design -max_num_inst 2000000 -min_num_inst 40000 \
-max_num_macro 12 -min_num_macro 4 \
-net_threshold 20 -virtual_weight 50 \
-num_hop 0 -timing_weight 1000 \
-report_file ${top_design}.hgr
exit
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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