| 一种基于强化学习的混合缓存能耗优化与评价 |
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| 引用本文： | 范浩, 徐光平, 薛彦兵, 高赞, 张桦. 一种基于强化学习的混合缓存能耗优化与评价[J]. 计算机研究与发展, 2020, 57(6): 1125-1139. DOI: 10.7544/issn1000-1239.2020.20200010 |
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| 作者姓名： | 范浩 徐光平 薛彦兵 高赞 张桦 |
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| 作者单位： | 1.1(天津理工大学计算机科学与工程学院 天津 300384);2.2(智能计算及软件新技术天津市重点实验室(天津理工大学) 天津 300384);3.3(天津中德应用技术大学 天津 300350) (fan-h@outlook.com) |
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| 基金项目： | 国家自然科学基金;天津市自然科学基金 |
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| 摘 要： | 新兴的非易失存储器STT-RAM具有低泄漏功率、高密度和快速读取速度、高写入能量等特点；而SRAM具有高泄漏功率、低密度、快速读取写入速度、低写入能量等特点.SRAM和STT-RAM相结合组成的混合缓存充分发挥了两者的性能，提供了比SRAM更低的泄漏功率和更高的单元密度，比STT-RAM更高的写入速度和更低的写入能量.混合缓存结构主要是通过把写密集数据放入SRAM中、读密集型数据放入STT-RAM中发挥这2种存储器的性能.因此如何识别并分配读写密集型数据是混合缓存设计的关键挑战.利用缓存访问请求的写入强度和重用信息，提出一种基于强化学习的缓存管理方法，设计缓存分配策略优化能耗.关键思想是使用强化学习对得到的缓存行(cache line)集合的能耗进行学习，得到该集合分配到SRAM或者STT-RAM的权重，将集合中的缓存行分配到权重大的区域.实验评估表明：提出的策略与以前的策略相比，在单核(四核)系统中能耗平均降低了16.9％(9.7％).
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| 关 键 词： | 强化学习 混合缓存架构 缓存 自旋转移力矩随机存取存储器 分配策略 |
An Energy Consumption Optimization and Evaluation for Hybrid Cache Based on Reinforcement Learning |
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| Fan Hao, Xu Guangping, Xue Yanbing, Gao Zan, Zhang Hua. An Energy Consumption Optimization and Evaluation for Hybrid Cache Based on Reinforcement Learning[J]. Journal of Computer Research and Development, 2020, 57(6): 1125-1139. DOI: 10.7544/issn1000-1239.2020.20200010 |
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| Authors: | Fan Hao Xu Guangping Xue Yanbing Gao Zan Zhang Hua |
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| Affiliation: | 1.1(School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384);2.2(Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology(Tianjin University of Technology), Tianjin 300384);3.3(Tianjin Sino-German University of Applied Sciences, Tianjin 300350) |
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| Abstract: | Emerging non-volatile memory STT-RAM has the characteristics of low leakage power, high density, fast read speed, and high write energy. Meanwhile, SRAM has the characteristics of high leakage power, low density, fast read and write speed, low write energy, etc. The hybrid cache of SRAM and STT-RAM fully utilizes the respective advantages of both memory medias, providing lower leakage power and higher cell density than SRAM, higher write speed and lower write energy than STT-RAM. The architecture of hybrid cache mainly achieves both of benefits by putting write-intensive data into SRAM and read-intensive data into STT-RAM. Therefore, how to identify and allocate read-write-intensive data is the key challenge for the hybrid cache design. This paper proposes a cache management method based on the reinforcement learning that uses the write intensity and reuse information of cache access requests to design a cache allocation policy and optimize energy consumption. The key idea is to use the reinforcement learning algorithm to get the weight for the set allocating to SRAM or STT-RAM by learning from the energy consumption of cache line sets. The algorithm allocates a cache line in a set to the region with greater weight. Evaluations show that our proposed policy reduces the average energy consumption by 16.9%(9.7%) in a single-core (quad-core) system compared with the previous policies. |
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| Keywords: | reinforcement learning hybrid cache architecture cache spin transfer torque random access memory allocation policy |
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