Optimization of convolutional backpropagation operators based on domestic accelerators

Fushuai Li; Zhan Yang; Yongqing Chen; Jingde Bu; Jinliang Jiang

doi:10.1117/12.2684767

21 July 2023 Optimization of convolutional backpropagation operators based on domestic accelerators

Fushuai Li, Zhan Yang, Yongqing Chen, Jingde Bu, Jinliang Jiang

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127171X (2023) https://doi.org/10.1117/12.2684767
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

Convolutional Neural Network (CNN) has always been a hot topic in deep learning. With the increasing demand for network models in daily production, the optimization of convolution calculation process is very important. This paper starts with the process of back propagation in convolutional neural network, introduces the derivation of convolutional neural network back propagation and the conversion process of im2col, uses implicit to convert the calculation of convolution on the domestic acceleration platform, and optimizes the convolution back propagation operator through a variety of general matrix multiplication optimization strategies. The final performance reaches more than 70% of the performance of NVIDIA operator, which meets the expectation of the experiment under the performance bottleneck of the platform.

Citation Download Citation

Fushuai Li, Zhan Yang, Yongqing Chen, Jingde Bu, and Jinliang Jiang "Optimization of convolutional backpropagation operators based on domestic accelerators", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127171X (21 July 2023); https://doi.org/10.1117/12.2684767

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