现代电子技术2025，Vol.48Issue(4)：52-56,5.DOI:10.16652/j.issn.1004-373x.2025.04.009
基于循环神经网络的多模态数据层次化缓存系统设计
Design of multimodal data hierarchical caching system based on recurrent neural network
摘要
Abstract
In order to improve the management effect of multimodal data,improve data access speed and reduce database load,a multimodal data hierarchical caching system based on recurrent neural networks is designed.In the DRAM/NVM hybrid memory module,DRAM is used to cache the main memory NVM.When there is a cache loss in DRAM,the high-speed acquisition card built-in in the access monitoring module is used to collect the historical access records of frequently accessed 4 KB data blocks on NVM.The historical access records are encoded as access vectors to construct a training set,which is used as input for the long short term memory network(LSTM)to predict access frequency.In the cache filtering module,the 4 KB multimodal data with predicted access frequency exceeding the set threshold is read into DRAM for caching.The experimental results show that the designed system can minimize the bandwidth usage of the system,and has low TLB miss rate,high cache execution efficiency,and significant caching advantages when facing large pages.关键词
多模态数据/层次化缓存/循环神经网络/长短期记忆(LSTM)网络/DRAM/NVM/访问频率Key words
multimodal data/hierarchical caching/recurrent neural network/long short term memory network/DRAM/NVM/access frequency分类
信息技术与安全科学引用本文复制引用
张燕..基于循环神经网络的多模态数据层次化缓存系统设计[J].现代电子技术,2025,48(4):52-56,5.基金项目
国家自然科学基金项目(41561100) （41561100）
新疆师范大学新疆自治区"十四五"重点学科招标课题(23XJKD0202) （23XJKD0202）
教育部产学研项目(220605350203448) （220605350203448）
新疆师范大学教改项目(SDJG2022-14) （SDJG2022-14）
