ChiPFormer
Transferable Chip Placement via Offline Decision Transformer
Yao Lai, Jinxin Liu, Zhentao Tang, Bin Wang, Jianye Hao, Ping Luo
The University of Hong Kong / Shanghai AI Lab / Huawei Noah's Lab / Zhejiang University / Tianjin University
ICML 2023
Introdution
ChiPFormer casts the chip placement as an offline RL formulation and enables learning a transferable placement policy from fixed offline data, which can improve the placement efficiency and quality (up to 97% time and 65% HPWL decrease).
Video
Model Architecture
Placement Process Animation
Phase 1
Place macros by ChiPFormer.
Chip: Adaptec3
Chip: Adaptec4
Phase 2
Fix macros, place standard cells in coarse.
Phase 3
Set macros and standard cells movable,
place all modules.
Placement Result Comparison
Chip: Adaptec3
Human
HPWL = 19.41e7Overlap = 0%ChiPFormer
HPWL = 13.97e7Overlap = 0%ChiPFormer: Adaptec4
Human
HPWL = 17.44e7Overlap = 0%DeepPR
HPWL = 23.40e7Overlap = 32.11%PRNet
HPWL = 23.64e7Overlap = 13.36%GraphPlace
HPWL = 25.58e7Overlap = 7.43%MaskPlace
HPWL = 22.97e7Overlap = 0%DREAMPlace
HPWL = 14.41e7Overlap = 0%Flora
HPWL = 14.30e7Overlap = 0%ChiPFormer
HPWL = 12.97e7Overlap = 0%Offline Dataset Release
We release the offline training data in Google Drive. (500 placement results for 12 chip circuits.)
Documents can be seen in Google Docs.
Code Release
We have released our code on github.