Reinforcement Learning-Based Optimization of Workload-Aware Power Delivery Networks
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-provisioned networks and inefficient use of resources. This paper presents a reinforcement learning-based framework for the optimization of workload-aware PDNs. The proposed methodology first generates workload-aware PDNs using architectural power traces obtained from system-level simulations. These power traces are mapped to spatial power density distributions, enabling adaptive allocation of PDN resources according...
| Authors | Oran Hayes, Maria Pantazi-Kypraiou, Athanasios Tziouvaras et al. |
|---|---|
| Primary category | cs.AR |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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