Rank-based Multi-objective Approximate Logic Synthesis via Monte Carlo Tree Search - Ye, Yuyang; Hu, Xiangfei; Liu, Yuchen; Xu, Peng; Gong, Yu; Chen, Tinghuan; Yan, Hao; Yu, Bei; Shi, Longxing (2025 62nd ACM/IEEE Design Automation Conference (DAC), 2025)
Approximate Logic Synthesis (ALS) is an automated technique designed for error-tolerant applications, optimizing delay, area, and power under specified error constraints. However, existing methods typically focus on either delay reduction or area minimization, often leading to local optima in multi-objective optimization. This paper proposes a rankbased multi-objective ALS framework using Monte Carlo Tree Search (MCTS). It develops non-dominated circuit ranking, to guide MCTS in exploring local approximate changes (LACs) across the entire circuit and generate approximate circuit sets with great optimization potential. Additionally, a Rank-Transformer model is introduced to predict pathdomain ranks, enhancing the application of high-quality LACs within circuit paths. Experimental results show that our framework achieves faster and more efficient optimization in delay and area simultaneously compared to state-of-the-art methods.