Retrieval-Guided Reinforcement Learning for Boolean Circuit Minimization

Animesh Basak Chowdhury, Marco Romanelli, Benjamin Tan, Ramesh Karri, Siddharth Garg

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Keywords: Electronics Design Automation (EDA), Logic Synthesis, Reinforcement Learning, Hardware design, Circuits

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TL;DR: We propose Retrieval Guided RL for logic synthesis to generalize for diverse hardware. Pre-trained agents, combined with MCTS fails on novel designs. We adjusts agent recommendations using nearest neighbor similarity scores for improved synthesis.

Abstract: Logic synthesis, a pivotal stage in chip design, entails optimizing chip specifications encoded in hardware description languages like Verilog into highly efficient implementations using Boolean logic gates. The process involves a sequential application of logic minimization heuristics (``synthesis recipe"), with their arrangement significantly impacting crucial metrics such as area and delay. Addressing the challenge posed by the broad spectrum of hardware design complexities — from variations of past designs (e.g., adders and multipliers) to entirely novel configurations (e.g., innovative processor instructions) — requires a nuanced 'synthesis recipe' guided by human expertise and intuition. This study conducts a thorough examination of learning and search techniques for logic synthesis, unearthing a surprising revelation: pre-trained agents, when confronted with entirely novel designs, may veer off course, detrimentally affecting the search trajectory. We present ABC-RL, a meticulously tuned $\alpha$ parameter that adeptly adjusts recommendations from pre-trained agents during the search process. Computed based on similarity scores through nearest neighbor retrieval from the training dataset, ABC-RL yields superior synthesis recipes tailored for a wide array of hardware designs. Our findings showcase substantial enhancements in the Quality of Result (QoR) of synthesized circuits, boasting improvements of up to 24.8\% compared to state-of-the-art techniques. Furthermore, ABC-RL achieves an impressive up to 9x reduction in runtime (iso-QoR) when compared to current state-of-the-art methodologies.

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Primary Area: infrastructure, software libraries, hardware, etc.

Submission Number: 7821