Workload-Aware Early-Stage Power Delivery Network Optimization via Architectural Power Traces
Abstract
Power Delivery Networks (PDNs) are critical for maintaining voltage integrity in modern multiprocessor systems. Conventional early-stage PDN planning relies on static or worst-case power assumptions, often leading to over-provisioned designs and inefficient use of routing resources. This paper proposes a workload-aware methodology for early-stage PDN optimization based on architectural power traces. Using architectural simulations, temporal power activity is captured at fine granularity and mapped to spatial power density distributions across the chip. These distributions are then translated into current demand profiles to guide PDN topology planning at tile granularity. By incorporating realistic workload behavior, the proposed approach enables adaptive PDN resource allocation during early design stages. Experimental results demonstrate that the method achieves up to 32.94% reduction in PDN metal area compared to conventional worst-case designs, while maintaining compliance with IR drop and electromigration constraints.
Cite
@article{arxiv.2605.17182,
title = {Workload-Aware Early-Stage Power Delivery Network Optimization via Architectural Power Traces},
author = {Oran Hayes and Maria Pantazi-Kypraiou and Athanasios Tziouvaras and George Stamoulis and Anuj Pathania and Shreejith Shanker and George Floros},
journal= {arXiv preprint arXiv:2605.17182},
year = {2026}
}
Comments
Accepted for publication at the SMACD 2026