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- W4312889722 abstract "Generating suboptimal synthesis transformation sequences (“synthesis recipe”) is an important problem in logic synthesis. Manually crafted synthesis recipes have poor quality. State-of-the art machine learning (ML) works to generate synthesis recipes do not scale to large netlists as the models need to be trained from scratch, for which training data is collected using time-consuming synthesis runs. We propose a new approach, Bulls-Eye, that fine-tunes a pretrained model on past synthesis data to accurately predict the quality of a synthesis recipe for an unseen netlist. Our approach achieves <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>$2times $ </tex-math></inline-formula> – <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>$30times $ </tex-math></inline-formula> runtime improvement and generates synthesis recipes achieving close to 95% quality-of-result (QoR) compared to conventional techniques using actual synthesis runs. We show our QoR beat state-of-the-art approaches on various benchmarks." @default.
- W4312889722 created "2023-01-05" @default.
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- W4312889722 date "2023-08-01" @default.
- W4312889722 modified "2023-10-16" @default.
- W4312889722 title "Bulls-Eye: Active Few-Shot Learning Guided Logic Synthesis" @default.
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- W4312889722 doi "https://doi.org/10.1109/tcad.2022.3226668" @default.
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