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Poster
MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning
Yao Lai · Yao Mu · Ping Luo

Wed Nov 30 09:00 AM -- 11:00 AM (PST) @ Hall J #423
in Poster Session 3 »

Placement is an essential task in modern chip design, aiming at placing millions of circuit modules on a 2D chip canvas. Unlike the human-centric solution, which requires months of intense effort by hardware engineers to produce a layout to minimize delay and energy consumption, deep reinforcement learning has become an emerging autonomous tool. However, the learning-centric method is still in its early stage, impeded by a massive design space of size ten to the order of a few thousand. This work presents MaskPlace to automatically generate a valid chip layout design within a few hours, whose performance can be superior or comparable to recent advanced approaches. It has several appealing benefits that prior arts do not have. Firstly, MaskPlace recasts placement as a problem of learning pixel-level visual representation to comprehensively describe millions of modules on a chip, enabling placement in a high-resolution canvas and a large action space. It outperforms recent methods that represent a chip as a hypergraph. Secondly, it enables training the policy network by an intuitive reward function with dense reward, rather than a complicated reward function with sparse reward from previous methods. Thirdly, extensive experiments on many public benchmarks show that MaskPlace outperforms existing RL approaches in all key performance metrics, including wirelength, congestion, and density. For example, it achieves 60%-90% wirelength reduction and guarantees zero overlaps. We believe MaskPlace can improve AI-assisted chip layout design. The deliverables are released at https://laiyao1.github.io/maskplace.

Keywords: Combinatorial Optimization · Applications · Reinforcement Learning · Electronics Design Automation
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Yao Lai (the University of Hong Kong)
Yao Mu (The University of Hong Kong)

I am currently a Ph.D. Candidate of Computer Science at the University of Hong Kong. I graduated with a Master Degree from Tsinghua University in June 2021. My research interests include Reinforcement Learning, Representation Learning, Autonomous Driving, Optimal Control, and Computer Vision.

Ping Luo (The University of Hong Kong)

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