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Reinforcement Learning-Based Optimization of Workload-Aware Power Delivery Networks

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The paper introduces a reinforcement learning framework for optimizing workload‑aware power delivery networks (PDNs) in VLSI chips. By mapping system‑level power traces to spatial power density distributions, the method adaptively allocates PDN resources and uses a Deep Q‑Network to perform wire‑width optimization, reducing PDN area while meeting electromigration and IR‑drop constraints. Experiments on multiprocessor floorplans with PARSEC and SPLASH‑2 workloads show a 47% area reduction and a 26× speedup over simulated annealing.

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