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OA北大核心CSTPCD

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%.