Flexible Multiple-Objective Reinforcement Learning for Chip Placement

Autor:	Chang, Fu-Chieh, Tseng, Yu-Wei, Yu, Ya-Wen, Lee, Ssu-Rui, Cioba, Alexandru, Tseng, I-Lun, Shiu, Da-shan, Hsu, Jhih-Wei, Wang, Cheng-Yuan, Yang, Chien-Yi, Wang, Ren-Chu, Chang, Yao-Wen, Chen, Tai-Chen, Chen, Tung-Chieh
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Recently, successful applications of reinforcement learning to chip placement have emerged. Pretrained models are necessary to improve efficiency and effectiveness. Currently, the weights of objective metrics (e.g., wirelength, congestion, and timing) are fixed during pretraining. However, fixed-weighed models cannot generate the diversity of placements required for engineers to accommodate changing requirements as they arise. This paper proposes flexible multiple-objective reinforcement learning (MORL) to support objective functions with inference-time variable weights using just a single pretrained model. Our macro placement results show that MORL can generate the Pareto frontier of multiple objectives effectively. Comment: A short version of this article is published in DAC'22:LBR (see ACM DOI 10.1145/3489517.3530617)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.06407 Zobrazit plný text záznamu View this record from Arxiv