计算机工程2023，Vol.49Issue(12)：55-62,70,9.DOI:10.19678/j.issn.1000-3428.0066172
基于帧间数据复用的稀疏CNN加速器设计
Design of Sparse CNN Accelerator Based on Inter-Frame Data Reuse
摘要
Abstract
Convolutional Neural Network(CNN)are widely used for object detection and other tasks in video applications.However,conventional CNN accelerators focus only on the acceleration of single-image inferences and do not use data redundancy between successive video frames to accelerate video tasks.CNN accelerators currently using inter-frame data reuse have low sparsity,large model size,and high computational complexity.To solve these problems,a design using a learned step-size low-precision quantization is proposed to increase the sparsity of differential frames.Furthermore,the power of two scales is proposed to implement hardware-friendly quantization.This design also uses the Winograd algorithm to reduce the computational complexity of the convolution operator.Based on this,an input-channel bitmap compression scheme is proposed to exploit the sparsity of both activations and weights to leverage full zero skipping.Based on the YOLOv3 tiny network,the proposed quantization method and sparse CNN accelerator are verified on a Field Programmable Gate Array(FPGA)platform using a subset of the ImageNet ILSVRC2015 VID and DAC2020 datasets.The results show that the proposed quantization method achieves 4-bit full-integer quantization with a loss of less than 2%in mean Average Precision(mAP).Owing to interframe data reuse,the designed sparse CNN accelerator achieves a performance of 814.2×109operation/s and an energy efficiency ratio of 201.1×109operation/s/W.Compared with other FPGA-based accelerators,the designed accelerator achieves 1.77-8.99 times higher performance and 1.91-5.56 times higher energy efficiency.关键词
卷积神经网络/低精度量化/帧间数据复用/Winograd算法/加速器/现场可编程门阵列Key words
Convolutional Neural Network(CNN)/low-precision quantization/inter-frame data reuse/Winograd algorithm/accelerator/Field Programmable Gate Array(FPGA)分类
信息技术与安全科学引用本文复制引用
洪起润,王琴..基于帧间数据复用的稀疏CNN加速器设计[J].计算机工程,2023,49(12):55-62,70,9.基金项目
国家重点研发计划(2018YFA0701500). （2018YFA0701500）
