Summary of Retrieval-guided Reinforcement Learning For Boolean Circuit Minimization, by Animesh Basak Chowdhury et al.
Retrieval-Guided Reinforcement Learning for Boolean Circuit Minimization
by Animesh Basak Chowdhury, Marco Romanelli, Benjamin Tan, Ramesh Karri, Siddharth Garg
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
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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 Logic synthesis is a crucial stage in chip design that involves optimizing chip specifications encoded in hardware description languages like Verilog into highly efficient implementations using Boolean logic gates. The process relies on a sequential application of logic minimization heuristics, with the arrangement of these heuristics significantly impacting metrics such as area and delay. This paper presents ABC-RL, a meticulously tuned α parameter that adjusts recommendations from pre-trained agents during the search process to produce superior synthesis recipes for a wide array of hardware designs. The authors find substantial enhancements in the Quality-of-result (QoR) of synthesized circuits, with improvements up to 24.8% compared to state-of-the-art techniques. ABC-RL also achieves an impressive up to 9x reduction in runtime (iso-QoR) when compared to current state-of-the-art methodologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Logic synthesis is a way to make chip designs more efficient and fast. It’s like solving a puzzle, where you try different solutions until you find the best one. But sometimes, even with pre-trained agents that have learned from lots of experience, they can get stuck or take too long to solve new puzzles. This paper presents a new approach called ABC-RL that helps these agents make better decisions and solve puzzles faster. It’s like having a special guide that adjusts its recommendations based on how well it knows the puzzle. The results show that this approach makes chips up to 24.8% more efficient and can solve problems up to 9 times faster than before. |
