Design and Optimization of Winograd Convolution on Array Accelerator

查看全文 作　　者：Ji [1]Lai;Lixin [2]Yang;Dejian [2]Li;Chongfei [2]Shen;Xi [2]Feng;Jizeng [3]Wei;Yu [1]Liu

机构地区：[1]School of Microelectronics,Tianjin University,Tianjin 300072,China;[2]Beijing Smart-Chip Microelectronics Technology Company Limited,Beijing 100192,China;[3]College of Intellegence and Computing,Tianjin University,Tianjin 300072,China

出　　处：《Journal of Beijing Institute of Technology》索引2023年第32卷第1期,共13页

基　　金：supported by the Project of the State Grid Corporation of China in 2022(No.5700-201941501A-0-0-00);the National Natural Science Foundation of China(No.U21B2031). 摘　　要：With the rapid development and popularization of artificial intelligence technology,convolutional neural network(CNN)is applied in many fields,and begins to replace most traditional algorithms and gradually deploys to terminal devices.However,the huge data movement and computational complexity of CNN bring huge power consumption and performance challenges to the hardware,which hinders the application of CNN in embedded devices such as smartphones and smart cars.This paper implements a convolutional neural network accelerator based on Winograd convolution algorithm on field-programmable gate array(FPGA).Firstly,a convolution kernel decomposition method for Winograd convolution is proposed.The convolution kernel larger than 3×3 is divided into multiple 3×3 convolution kernels for convolution operation,and the unsynchronized long convolution operation is processed.Then,we design Winograd convolution array and use configurable multiplier to flexibly realize multiplication for data with different accuracy.Experimental results on VGG16 and AlexNet network show that our accelerator has the most energy efficient and 101 times that of the CPU,5.8 times that of the GPU.At the same time,it has higher energy efficiency than other convolutional neural network accelerators. 关键词：convolutional neural network Winograd convolution algorithm ACCELERATOR

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Design and Optimization of Winograd Convolution on Array Accelerator

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