English(EN) Reinforcement Learning-Based Optimization of Workload-Aware Power Delivery Networks

强化学习优化VLSI芯片供电网络

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 11:19

研究人员开发了一个强化学习框架，用于优化超大规模集成（VLSI）芯片中的供电网络（PDN）。这种新方法使用感知工作负载的功耗轨迹来调整PDN资源分配，在保持完整性约束的同时，将平均归一化PDN面积减少了47%。该强化学习代理，特别是深度Q网络，实现了与模拟退火相当的优化质量，但速度显著更快，优化完成速度大约快26倍。 AI

影响这种方法可以通过减少供电网络中的过度配置，从而实现更高效、更小型的VLSI芯片。

排序理由该项目是一篇研究论文，详细介绍了使用强化学习优化VLSI芯片供电网络的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-10-05 11:19

基于强化学习的感知工作负载的功率传输网络优化

Power Delivery Networks (PDNs) are critical components of modern VLSI chips, providing stable voltage levels while satisfying electromigration (EM) and IR-drop constraints. Conventional PDN design methodologies typically rely on worst-case assumptions, often resulting in over-pro…

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基于强化学习的感知工作负载的功率传输网络优化

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