基于强化学习的工作负载感知供电网络优化
Reinforcement Learning-Based Optimization of Workload-Aware Power Delivery Networks
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中文总结 AI 辅助
本文提出基于强化学习的框架优化工作负载感知供电网络，利用DQN智能体进行线宽优化，在满足EM和IR约束下减少47%的PDN面积，且优化速度比模拟退火快约26倍。
中文摘要 AI 辅助
供电网络（PDNs）是现代超大规模集成（VLSI）芯片的关键组成部分，在满足电迁移（EM）和IR压降约束的同时提供稳定的电压水平。传统PDN设计方法通常依赖于最坏情况假设，往往导致网络过度配置和资源利用效率低下。本文提出了一种基于强化学习的框架，用于优化工作负载感知的PDN。所提出的方法首先利用从系统级仿真中获得的架构功耗轨迹生成工作负载感知的PDN。这些功耗轨迹被映射为空间功率密度分布，从而能够根据局部电流需求自适应地分配PDN资源。随后，强化学习智能体执行线宽优化，以在保持EM和电压完整性约束的同时最小化PDN面积。电气和可靠性指标通过基于SPICE的电路分析和EM寿命估计获得。实验评估在由4核、8核和16核多处理器布局生成的工作负载感知PDN数据集上进行，使用PARSEC和SPLASH-2基准工作负载。此外，所提出的基于深度Q网络（DQN）的优化器在满足所有EM和IR压降约束的同时，将平均归一化PDN面积减少了47%。与模拟退火相比，所提出的方法在实现相当优化质量的同时，提供了约26倍的优化速度提升。
英文摘要
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.
发表机构
- Trinity College Dublin（都柏林圣三一学院）
- University of Thessaly（色萨利大学）
- University of Amsterdam（阿姆斯特丹大学）
机构由 AI 辅助整理，请以论文原文为准。
