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About this paper
Mingju Liu, Daniel Robinson, Yingjie Li, Cunxi Yu
MapTune, a reinforcement learning framework, optimizes Application-Specific Integrated Circuit (ASIC) technology mapping by intelligently selecting subsets of cells from large technology libraries. The approach achieved an average 22.54% improvement in Area-Delay Product (ADP) across various benchmarks and technology nodes, consistently outperforming traditional methods using complete libraries.
Key insights
- Randomly sampled subsets of technology libraries can frequently outperform the complete library in terms of Area-Delay Product (ADP), indicating potential for optimization through intelligent cell selection.
- Reinforcement learning algorithms, specifically Multi-Armed Bandit (MAB) approaches like epsilon-greedy and UCB, can effectively learn optimal cell subsets for technology mapping by managing cell probabilities and integrating feedback.
- Automating cell selection with ML can reduce manual effort and achieve better results than traditional expert-driven approaches for ASIC technology mapping.
Summary by alphaXiv.
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