Optimized Compression for Implementing Convolutional Neural Networks on FPGA

• Published in: Electronics (Basel) Vol. 8; no. 3; p. 295
• Main Authors: Zhang, Min, Li, Linpeng, Wang, Hai, Liu, Yan, Qin, Hongbo, Zhao, Wei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 06.03.2019
• Subjects: Accuracy; Algorithms; Artificial neural networks; Central processing units; Compressibility; Computer memory; CPUs; Deep learning; Design; Energy efficiency; Field programmable gate arrays; Graphics processing units; Network latency; Neural networks; Parameters; Pruning; Recurrent neural networks
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics8030295

Field programmable gate array (FPGA) is widely considered as a promising platform for convolutional neural network (CNN) acceleration. However, the large numbers of parameters of CNNs cause heavy computing and memory burdens for FPGA-based CNN implementation. To solve this problem, this paper proposes an optimized compression strategy, and realizes an accelerator based on FPGA for CNNs. Firstly, a reversed-pruning strategy is proposed which reduces the number of parameters of AlexNet by a factor of 13× without accuracy loss on the ImageNet dataset. Peak-pruning is further introduced to achieve better compressibility. Moreover, quantization gives another 4× with negligible loss of accuracy. Secondly, an efficient storage technique, which aims for the reduction of the whole overhead cache of the convolutional layer and the fully connected layer, is presented respectively. Finally, the effectiveness of the proposed strategy is verified by an accelerator implemented on a Xilinx ZCU104 evaluation board. By improving existing pruning techniques and the storage format of sparse data, we significantly reduce the size of AlexNet by 28×, from 243 MB to 8.7 MB. In addition, the overall performance of our accelerator achieves 9.73 fps for the compressed AlexNet. Compared with the central processing unit (CPU) and graphics processing unit (GPU) platforms, our implementation achieves 182.3× and 1.1× improvements in latency and throughput, respectively, on the convolutional (CONV) layers of AlexNet, with an 822.0× and 15.8× improvement for energy efficiency, separately. This novel compression strategy provides a reference for other neural network applications, including CNNs, long short-term memory (LSTM), and recurrent neural networks (RNNs).