计算机工程与科学2024，Vol.46Issue(4)：580-589,10.DOI:10.3969/j.issn.1007-130X.2024.04.002
面向多核向量加速器的卷积神经网络推理和训练向量化方法
Convolutional neural network inference and training vectorization method for multicore vector accelerators
摘要
Abstract
With the widespread application of deep learning,represented by convolutional neural net-works(CNNs),the computational requirements of neural network models have increased rapidly,driv-ing the development of deep learning accelerators.The research focus has shifted to how to accelerate and optimize the performance of neural network models based on the architectural characteristics of ac-celerators.For the VGG network model inference and training algorithms on the independently designed multi core vector accelerator FT-M7004,vectorized mapping methods for core operators such as convo-lution,pooling,and fully connected layers are proposed.Optimization strategies,including SIMD vec-torization,DMA double-buffered transfer,and weight sharing,are employed to fully exploit the archi-tectural advantages of the vector accelerator,achieving high computational efficiency.Experimental re-sults indicate that on the FT-M7004 platform,the average computational efficiency for convolution layer inference and training is 86.62%and 69.63%,respectively;for fully connected layer inference and training,the average computational efficiency reaches 93.17%and 81.98%,respectively.The inference computational efficiency of the VGG network model on FT-M7004 exceeds that on the GPU platform by over 20%.关键词
多核向量加速器/卷积神经网络/推理算法/训练算法Key words
multicore vector accelerator/convolutional neural network/inference algorithm/training algorithm分类
信息技术与安全科学引用本文复制引用
陈杰,李程,刘仲..面向多核向量加速器的卷积神经网络推理和训练向量化方法[J].计算机工程与科学,2024,46(4):580-589,10.基金项目
并行与分布处理国家重点实验室基金(2021-KJWPDL-11) （2021-KJWPDL-11）
