[
    {
        "id": "osp-17090",
        "type": "article-journal",
        "title": "Reinforcement Learning-Based Optimization of Workload-Aware Power Delivery Networks",
        "author": [
            {
                "family": "Hayes",
                "given": "Oran"
            },
            {
                "family": "Pantazi-Kypraiou",
                "given": "Maria"
            },
            {
                "family": "Tziouvaras",
                "given": "Athanasios"
            },
            {
                "family": "Stamoulis",
                "given": "George"
            },
            {
                "family": "Pathania",
                "given": "Anuj"
            },
            {
                "family": "Shanker",
                "given": "Shreejith"
            },
            {
                "family": "Floros",
                "given": "George"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/reinforcement-learning-based-optimization-of-workload-aware-power-delivery-networks",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "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."
    }
]