الملخص
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 to local current demand. A reinforcement learning agent then performs wire-width optimization to minimize PDN area while maintaining EM and voltage integrity constraints. Electrical and reliability metrics are obtained using SPICE-based circuit analysis and EM lifetime estimation. Experimental evaluation is performed on a dataset of workload-aware PDNs generated from 4-, 8-, and 16-core multiprocessor floorplans using PARSEC and SPLASH-2 benchmark workloads. Furthermore, the proposed Deep Q-Network (DQN)-based optimizer reduces the average normalized PDN area by 47\% while satisfying all EM and IR-drop constraints. Compared to simulated annealing, the proposed approach achieves comparable optimization quality while providing approximately 26$\times$ faster optimization.
الكلمات المفتاحية
الموضوع
بيانات النشر
- المجلة
- غير متاح
- وصول مفتوح
- وصول مفتوح أخضر
اقتبس هذه المقالة
APA 7
Hayes, O., Pantazi-Kypraiou, M., Tziouvaras, A., Stamoulis, G., Pathania, A., Shanker, S., & Floros, G. (2026). Reinforcement Learning-Based Optimization of Workload-Aware Power Delivery Networks. https://omanscience.com/ar/articles/reinforcement-learning-based-optimization-of-workload-aware-power-delivery-networks
MLA 9
Hayes, Oran, et al. "Reinforcement Learning-Based Optimization of Workload-Aware Power Delivery Networks." https://omanscience.com/ar/articles/reinforcement-learning-based-optimization-of-workload-aware-power-delivery-networks.
شيكاغو (المؤلف–التاريخ)
Hayes, Oran, Maria Pantazi-Kypraiou, Athanasios Tziouvaras, George Stamoulis, Anuj Pathania, Shreejith Shanker, and George Floros. 2026. "Reinforcement Learning-Based Optimization of Workload-Aware Power Delivery Networks." https://omanscience.com/ar/articles/reinforcement-learning-based-optimization-of-workload-aware-power-delivery-networks.
هارفارد
Hayes, O., Pantazi-Kypraiou, M., Tziouvaras, A., Stamoulis, G., Pathania, A., Shanker, S. and Floros, G. (2026) 'Reinforcement Learning-Based Optimization of Workload-Aware Power Delivery Networks', Available at: https://omanscience.com/ar/articles/reinforcement-learning-based-optimization-of-workload-aware-power-delivery-networks.
فانكوفر
Hayes O, Pantazi-Kypraiou M, Tziouvaras A, Stamoulis G, Pathania A, Shanker S, et al. Reinforcement Learning-Based Optimization of Workload-Aware Power Delivery Networks. https://omanscience.com/ar/articles/reinforcement-learning-based-optimization-of-workload-aware-power-delivery-networks
IEEE
O. Hayes, M. Pantazi-Kypraiou, A. Tziouvaras, G. Stamoulis, A. Pathania, S. Shanker, and G. Floros, "Reinforcement Learning-Based Optimization of Workload-Aware Power Delivery Networks," https://omanscience.com/ar/articles/reinforcement-learning-based-optimization-of-workload-aware-power-delivery-networks.