基于3D-Winograd的快速卷积算法设计及FPGA实现

In recent years, Convolutional Neural Networks (CNNs) have been widely adopted by computer vision tasks. Due to the high performance, energy efficiency, and reconfigurability of FPGA, it has been considered as the most promising CNN hardware accelerator. However, the existing FPGA solutions based on the traditional Winograd method are usually limited by FPGA computing power and storage resources, and there is room for improvement in performance of 3D convolution operations. This paper first studied the one-dimensional expansion process of the Winograd algorithm suitable for three-dimensional operations; then, improved the performance of CNN on FPGA by increasing the one-time input feature map and the dimensional size of the convolution block, low-bit quantization weight and input data. The optimization ideas include four parts: the method of using shift instead of partial division, the division of tiles, the expansion of two-dimensional to three-dimensional, and low-bit quantization. Compared with the traditional two-dimensional Winograd algorithm, the number of clock cycles of each convolutional layer of the optimized algorithm is reduced by about 7 times, which is about 7 times less for each convolutional layer than the traditional sliding window convolution algorithm. Through the research, it is proved that the 3D-Winograd algorithm based on one-dimensional expansion can greatly reduce the computational complexity and improve the performance of running CNN on FPGA.