50 lines
1.7 KiB
Python
50 lines
1.7 KiB
Python
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import sys
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from utils import *
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from src import Flute, load_dataset, GPUTimer
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from main import get_option
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def main():
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Flute.register(8)
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# Read input file
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design_name = "example"
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params = {
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"benchmark": "custom",
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"design_name": "test",
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"lef": f"{design_name}/NangateOpenCellLibrary.lef",
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"lib": f"{design_name}/NangateOpenCellLibrary.lib",
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"def": f"{design_name}/example.def",
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"verilog": f"{design_name}/example.v",
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"sdc": f"{design_name}/example.sdc",
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"spef": f"{design_name}/example.spef",
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}
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args = get_option()
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logger = setup_logger(args, sys.argv)
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data, rawdb, gpdb = load_dataset(args, logger, params)
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device = torch.device(
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"cuda:{}".format(args.gpu) if torch.cuda.is_available() else "cpu"
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)
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data = data.to(device)
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data = data.preprocess()
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gputimer = GPUTimer(data, rawdb, gpdb, params, args)
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# timing analysis for extracted RC network
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gputimer.timer.read_spef(params["spef"])
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gputimer.update_timing_spef()
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wns_early, tns_early, wns_late, tns_late = gputimer.report_timing_slack()
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logger.info("SPEf evaluation: wns_early: %.3f, tns_early: %.3f, wns_late: %.3f, tns_late: %.3f" % (wns_early, tns_early, wns_late, tns_late))
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# timing analysis for normalized FLUTE RC tree
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gputimer.update_timing_eval(data.node_pos)
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wns_early, tns_early, wns_late, tns_late = gputimer.report_timing_slack()
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logger.info("Flute Tree Evaluation wns_early: %.3f, tns_early: %.3f, wns_late: %.3f, tns_late: %.3f" % (wns_early, tns_early, wns_late, tns_late))
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# run main
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__name__ == "__main__" and main()
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