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基于强化学习的多核芯片动态功耗管理框架

卓成 曾旭东 陈宇飞 孙凇昱 罗国杰 贺青 尹勋钊

卓成, 曾旭东, 陈宇飞, 孙凇昱, 罗国杰, 贺青, 尹勋钊. 基于强化学习的多核芯片动态功耗管理框架[J]. 电子与信息学报, 2023, 45(1): 24-32. doi: 10.11999/JEIT220350
引用本文: 卓成, 曾旭东, 陈宇飞, 孙凇昱, 罗国杰, 贺青, 尹勋钊. 基于强化学习的多核芯片动态功耗管理框架[J]. 电子与信息学报, 2023, 45(1): 24-32. doi: 10.11999/JEIT220350
ZHUO Cheng, ZENG Xudong, CHEN Yufei, SUN Songyu, LUO Guojie, HE Qing, YIN Xunzhao. Multi-core Chip Dynamic Power Management Framework Based on Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2023, 45(1): 24-32. doi: 10.11999/JEIT220350
Citation: ZHUO Cheng, ZENG Xudong, CHEN Yufei, SUN Songyu, LUO Guojie, HE Qing, YIN Xunzhao. Multi-core Chip Dynamic Power Management Framework Based on Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2023, 45(1): 24-32. doi: 10.11999/JEIT220350

基于强化学习的多核芯片动态功耗管理框架

doi: 10.11999/JEIT220350
基金项目: 浙江省重点研发计划(2020C01052),国家自然科学基金(61974133, 62034007, 62141404)
详细信息
    作者简介:

    卓成:男,研究员,研究方向为低功耗芯片设计、人工智能算法及硬件加速、3D芯片设计及优化

    曾旭东:男,硕士生,研究方向为深度学习算法及智能系统设计

    陈宇飞:男,博士生,研究方向为电源完整性分析

    罗国杰:男,研究员,研究方向为电子设计自动化、基于FPGA及新型器件的异构计算

    贺青:男,博士,研究方向为新型EDA设计

    尹勋钊:男,研究员,研究方向为新型器件、电路、架构跨层协同设计

    通讯作者:

    卓成 czhuo@zju.edu.cn

  • 中图分类号: TN402; TP315

Multi-core Chip Dynamic Power Management Framework Based on Reinforcement Learning

Funds: Zhejiang Provincial Key R&D program (2020C01052), The National Natural Science Foundation of China (61974133, 62034007, 62141404)
  • 摘要: 多核芯片可以为移动智能终端提供强大算力,但功耗和温度问题始终制约着其性能表现。针对这个问题,该文提出了一种基于强化学习的多核芯片动态功耗管理框架。首先,建立了一个基于GEM5的多核芯片动态电压频率调节仿真系统。然后,采用了一种考虑CMOS芯片物理特性的功耗模型构建方法以实现在线实时功耗监测。最后,设计了一种面向多核芯片的梯度式奖励方法,并使用深度Q神经网络(Deep Q Network, DQN)算法对多核芯片的功耗管理策略进行学习。仿真结果表明,相比于常规的Ondemand,MaxBIPS方案,该文所提出的框架分别实现了2.12%, 4.03%的多核芯片计算性能提升。
  • 图  1  多核芯片动态电压频率调节仿真系统

    图  2  多核芯片动态功耗管理框架

    图  3  拟合优度随硬件事件的筛选而增长

    图  4  功耗模型在测试集上的表现

    图  5  动态功耗管理算法的训练框架与流程

    图  6  历史回放机制

    图  7  DDPMF中动态功耗管理算法的训练过程

    图  8  3种动态功耗管理方案(或策略)在多核芯片DVFS仿真系统上的性能对比实验结果

    表  1  梯度式奖励

    中等性能指令
    执行数(百万条)
    指令数梯度$G$
    (百万条)
    奖励梯度$R$
    ${I_{{\rm{bench}}} }$$ + {g_0}$${r_0}$
    $+ g_1$${r_1}$
    $ \vdots$ $\vdots$
    $+ g_n$${r_n}$
    下载: 导出CSV

    表  2  环境奖励梯度

    中等性能指令执行数
    (百万条)
    指令数梯度$G$
    (百万条)
    奖励梯度$R$
    8322–222+1
    –122+10
    –68+100
    –22+1000
    +28+10000
    +78+100000
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-31
  • 修回日期:  2022-06-17
  • 网络出版日期:  2022-06-29
  • 刊出日期:  2023-01-17

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