Summary of Learning-driven Physically-aware Large-scale Circuit Gate Sizing, by Yuyang Ye et al.
Learning-driven Physically-aware Large-scale Circuit Gate Sizing
by Yuyang Ye, Peng Xu, Lizheng Ren, Tinghuan Chen, Hao Yan, Bei Yu, Longxing Shi
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Hardware Architecture (cs.AR); Emerging Technologies (cs.ET)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel framework for gate sizing in timing optimization after physical design, addressing limitations of existing machine learning-based approaches. The authors’ learning-driven physically-aware gate sizing framework optimizes timing performance on large-scale circuits efficiently, leveraging gradient descent optimization and a multi-modal gate sizing-aware timing model that jointly learns timing information from multiple paths and physical layout constraints. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to optimize the way electronic circuits work. Right now, there are problems with how we size the tiny parts (called gates) in these circuits. The authors want to fix this by creating a new way of doing things that takes into account both how the circuit works and what it looks like on a physical level. They’re trying to make their method better than what’s already out there, so that electronic circuits can work faster and more efficiently. |
Keywords
* Artificial intelligence * Gradient descent * Machine learning * Multi modal * Optimization
