← 研踪 PaperTrailarXiv:2610.34838 · v1
PaFeDSE: Pareto-Feedback-Guided Microarchitecture Design Space Exploration for Efficient Pareto Search
首次提交 2026-10-26 12:36
版本更新 2026-10-26 12:36
雷达观测 2026-10-29 00:36
摘要
Efficient microarchitecture design space exploration (DSE) is critical for optimizing performance, power, and area trade-offs in modern out-of-order processors. While machine-learning-based DSE significantly reduces the cost of exhaustively evaluating large design spaces, existing methods still waste expensive simulations on Pareto-miss candidates that do not improve the Pareto frontier and repeatedly process redundant candidates during iterative exploration. In this paper, we propose PaFeDSE, a Pareto-feedback-guided microarchitecture DSE framework for efficient Pareto search. PaFeDSE leverages Pareto-hit feedback to prioritize candidates likely to improve the frontier and Pareto-miss feedback to suppress exploration of unpromising local regions, while filtering redundant candidates that remain low priority across iterations. Evaluated on SPEC CPU 2017 workloads, PaFeDSE requires up to 49.0% fewer simulations than the strongest prior baseline to reach target hypervolume levels, while achieving a 7.1x reduction in exploration overhead through adaptive candidate filtering.
作者备注：8 pages, 9 figures. Accepted to ASP-DAC 2027
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v1 · 2026-10-26 12:36 · 最新观测版本
PaFeDSE: Pareto-Feedback-Guided Microarchitecture Design Space Exploration for Efficient Pareto Search
Efficient microarchitecture design space exploration (DSE) is critical for optimizing performance, power, and area trade-offs in modern out-of-order processors. While machine-learning-based DSE significantly reduces the cost of exhaustively evaluating large design spaces, existing methods still waste expensive simulations on Pareto-miss candidates that do not improve the Pareto frontier and repeatedly process redundant candidates during iterative exploration. In this paper, we propose PaFeDSE, a Pareto-feedback-guided microarchitecture DSE framework for efficient Pareto search. PaFeDSE leverages Pareto-hit feedback to prioritize candidates likely to improve the frontier and Pareto-miss feedback to suppress exploration of unpromising local regions, while filtering redundant candidates that remain low priority across iterations. Evaluated on SPEC CPU 2017 workloads, PaFeDSE requires up to 49.0% fewer simulations than the strongest prior baseline to reach target hypervolume levels, while achieving a 7.1x reduction in exploration overhead through adaptive candidate filtering.
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