Rank-based Multi-objective Approximate Logic Synthesis via Monte Carlo Tree Search

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-...

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Veröffentlicht in:2025 62nd ACM/IEEE Design Automation Conference (DAC) S. 1 - 7
Hauptverfasser: Ye, Yuyang, Hu, Xiangfei, Liu, Yuchen, Xu, Peng, Gong, Yu, Chen, Tinghuan, Yan, Hao, Yu, Bei, Shi, Longxing
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 22.06.2025
Schlagworte:
Costs
Delays
Design automation
Integrated circuit modeling
Logic
Minimization
Monte Carlo methods
Optimization
Predictive models
Runtime
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Zusammenfassung: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.
DOI:10.1109/DAC63849.2025.11132589