深度学习在多核缓存预取中的应用研究综述  

Review of deep learning-based multi-core cache prefetching research

作　　者：张建勋[1] 乔欣雨 林炳辉 Zhang Jianxun;Qiao Xinyu;Lin Binghui(School of Information Technology Engineering,Tianjin University of Technology and Education,Tianjin 300222,China)

机构地区：[1]天津职业技术师范大学信息技术工程学院,天津300222

出　　处：《计算机应用研究》2024年第2期341-347,共7页Application Research of Computers

基　　金：中国高校产学研自然基金资助项目(2021FNA04016)。

摘　　要：当前人工智能技术应用于系统结构领域的研究前景广阔,特别是将深度学习应用于多核架构的数据预取研究已经成为国内外的研究热点。针对基于深度学习的缓存预取任务进行了研究,形式化地定义了深度学习缓存预取模型。在介绍当前常见的多核缓存架构和预取技术的基础上,全面分析了现有基于深度学习的典型缓存预取器的设计思路。深度学习神经网络在多核缓存预取领域的应用主要采用了深度神经网络、循环神经网络、长短期记忆网络和注意力机制等机器学习方法,综合对比分析现有基于深度学习的数据预取神经网络模型后发现,基于深度学习的多核缓存预取技术在计算成本、模型优化和实用性等方面还存在着局限性,未来在自适应预取模型以及神经网络预取模型的实用性方面还有很大的研究探索空间和发展前景。The current research on the application of artificial intelligence techniques to the field of system architecture is promising,especially the research on applying deep learning to data prefetching in multicore architectures has become a research hotspot at home and abroad.This work studied the cache prefetching task based on deep learning and defined the deep learning cache prefetch model formally.Based on the introduction of current common multi-core cache architectures and prefetching techniques,this paper comprehensively analyzed the design ideas of existing typical cache prefetchers based on deep learning.The application of deep learning neural network in the field of multicore cache prefetching mainly adopts machine learning methods such as deep neural network,recurrent neural network,long and short-term memory network and attention mechanism.A comprehensive comparative analysis of existing deep learning-based data prefetching hierarchical neural models reveals that deep learning-based multicore cache prefetching techniques still have certain computational cost,model optimization,and practicality.In the future,there is still much room for research exploration and development prospect in adaptive prefetching models and the practicality of neural network prefetching models.

关 键 词：深度学习 数据预取 多核架构 缓存优化 神经网络 研究综述 

分 类 号：TP333[自动化与计算机技术—计算机系统结构] TP181[自动化与计算机技术—计算机科学与技术]