Design and implementation of reconfigurable CNN accelerator architecture based on elastic storage  

作　　者：Shan Rui Xu Jianing Huo Ziqing 

机构地区：[1]College of Electronic Engineering,Xi'an University of Post and Telecommunications,Xi'an 710121,China

出　　处：《The Journal of China Universities of Posts and Telecommunications》2025年第1期74-87,共14页中国邮电高校学报(英文版)

基　　金：supported by the National Science and Technology Major Project (2022ZD0119001);the National Natural Science Foundation of China (61834005,61802304);the Shaanxi Provincial Key Research and Development Plan(2024GX-YBXM-100)。

摘　　要：With the rapid iteration of neural network algorithms, higher requirements were placed on the computational performance and memory access bandwidth of neural network accelerators. Simply increasing bandwidth cannot improve energy efficiency, so improving the data reuse rate is a hot research topic. From the perspective of supporting data reuse, a reconfigurable convolutional neural network(CNN) accelerator based on elastic storage(RCAES) was designed in this paper. Supporting elastic memory access and flexible data flow reduces data movement between the processor and memory, eases the bandwidth pressure and enhances CNN acceleration performance. The experimental results indicate that by conducting 1×1 convolution and 3×3 convolution when performing convolution calculations, the execution speed increased by 25.00% and 61.61%, respectively. The 3×3 maximum pooling speed was increased by 76.04%.

关 键 词：RECONFIGURABLE array processor distributed storage neural network accelerator data reuse 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]