Presentation
Rank-based Multi-objective Approximate Logic Synthesis via Monte Carlo Tree Search
DescriptionApproximate 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 rank-based 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 path-domain 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.
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 rank-based 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 path-domain 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.
Event Type
Research Manuscript
TimeTuesday, June 245:00pm - 5:15pm PDT
Location3006, Level 3
EDA
EDA5: RTL/Logic Level and High-level Synthesis