NeurIPS'23 Macro Placement by Wire-Mask-Guided Black-Box Optimization

Official implementation of NeurIPS'23 paper "Macro Placement by Wire-Mask-Guided Black-Box Optimization"

This repository contains the Python code for WireMask-BBO, a black-box optimization framework for macro placement by using a wire-mask-guided greedy procedure for objective evaluation. Equipped with different BBO algorithms, WireMask-BBO empirically achieves significant improvements over previous methods.

Requirements

• gpytorch==1.8.1
• matplotlib==2.2.3
• numpy==1.15.1
• opencv_python==4.1.2.30
• scipy==1.1.0
• setuptools==40.2.0
• torch==1.13.1

File structure

• result directory stores the output results of optimzation. result/MaskPlace placement results are provided by the author of MaskPlace [https://openreview.net/forum?id=T2DBbSh6_uY].

• ISPD2005.py serves the benchmark download script.

• place_db.py serves the netlist information extraction script, originally borrowed from [https://github.com/laiyao1/maskplace].

• common.py defines several constants.

• utils.py defines functions to be used for optimization.

• TuRBO directory is borrowed from [https://github.com/uber-research/TuRBO] for TuRBO implemention for WireMask-BO.

• EA_swap_only.py , RS.py and BO.py are the main entrances for WireMask-EA, WireMask-RS and WireMask-BO optimization procedure.

• EA_finetune.py implements the finetuning procedure based on MaskPlace [https://openreview.net/forum?id=T2DBbSh6_uY] placement results.

• plot.py provides a script for reproducing the Figure 4 (HPWL v.s. wall clock time) in the main paper.

Usage

You should first build the environment according to the requirements.

Then download the ISPD2005 benchmark.

python ispd2005.py
mv ispd2005 benchmark

To run WireMask-EA on chip $adaptec1$ using random seed 2023, with 100 rounds of random search for initialzation, run the following code

python EA_swap_only.py --dataset adaptec1 --seed 2023 --init_round 100

To run WireMask-RS on chip $bigblue1$ using random seed 2024, with 200 rounds of random search, run the following code

python RS.py --dataset bigblue1 --seed 2024 --stop_round 200

To run WireMask-BO on chip $adaptec3$ using random seed 2025, run the following code

python BO.py --dataset adaptec1 --seed 2025

To finetune MaskPlace on chip $adaptec4$ using 1000 rounds of WireMask-EA, with random seed 2026, run the following code

python EA_finetune.py --dataset adaptec4 --seed 2026 --stop_round 1000

To reproducing the Figure 4 (HPWL v.s. wall clock time) in the main paper, you should first run EA, RS and BO for minutes, respectively. Then run

python plot.py

The results will be shown in all.pdf.

It is worth noting that our evaluation functions are included in utils.py. All results in our paper are generated based on cal_hpwl() and write_placement_and_overlap().