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 RGLS, 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, RGLS 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, RGLS achieves an impressive up to 9x reduction in runtime (iso-QoR) when compared to current state-of-the-art methodologies.
Retrieval-Guided Reinforcement Learning for Boolean Circuit Minimization
Animesh Basak Chowdhury Marco Romanelli Benjamin Tan Ramesh Karri Siddharth Garg