集成电路与嵌入式系统2024，Vol.24Issue(2)：57-63,7.
基于强化学习的阈值电压分配漏功耗优化方法研究
Research on reinforcement learning based leakage power optimization framework(RL-LPO)
摘要
Abstract
With the evolution of advanced technology,the proportion of leakage power consumption in the total power consumption of in-tegrated circuits continues to increase,which has gradually become one of the important factors restricting the reduction of circuit power consumption.Among the existing leakage power optimization methods,the method based on threshold voltage allocation has an exponen-tial power optimization effect and has no influence on the layout and routing,so it is widely adopted.However,in commercial signoff tools,in order to maintain pseudolinear complexity,the global search made by the underlying algorithm is limited,which makes it difficult to obtain optimal results.In this paper,a joint optimization framework RL-LPO based on graph neural network and reinforcement learning is proposed to achieve efficient gate unit threshold voltage distribution.In RL-LPO,the timing and physical information of the graph neural network GraphSAGE encoding circuit are used to aggregate the target unit and its local neighborhood information.Using the Deep Deterministic Policy Gradient(DDPG)reinforcement learning algorithm,the threshold voltage allocation is carried out consid-ering the leakage power consumption and timing variation under the guidance of the reward function.The gate unit threshold voltage dis-tribution framework RL-LPO proposed in this paper is verified by IWLS2005 and Opencores reference circuits under the 28 nm process,and compared with commercial signoff tools,RL-LPO reduces the additional leakage power consumption by at least 2.1％and achieves at least 4.2 times acceleration without adding timing violations.关键词
漏功耗优化/阈值电压/图神经网络/强化学习Key words
leakage power consumption optimization/threshold voltage/graph neural network/reinforcement learning分类
信息技术与安全科学引用本文复制引用
张展华,王家豪,丁文杰,曹鹏..基于强化学习的阈值电压分配漏功耗优化方法研究[J].集成电路与嵌入式系统,2024,24(2):57-63,7.基金项目
国家自然科学基金(62174031). （62174031）
