FPGA-based acceleration for binary neural networks in edge computing

As a core component in intelligent edge computing, deep neural networks (DNNs) will increasingly play a critically important role in addressing the intelligence-related issues in the industry domain, like smart factories and autonomous driving. Due to the requirement for a large amount of storage space and computing resources, DNNs are unfavorable for resource-constrained edge computing devices, especially for mobile terminals with scarce energy supply. Binarization of DNN has become a promising technology to achieve a high performance with low resource consumption in edge computing. Field-programmable gate array (FPGA)-based acceleration can further improve the computation efficiency to several times higher compared with the central processing unit (CPU) and graphics processing unit (GPU). This paper gives a brief overview of binary neural networks (BNNs) and the corresponding hardware accelerator designs on edge computing environments, and analyzes some significant studies in detail. The performances of some methods are evaluated through the experiment results, and the latest binarization technologies and hardware acceleration methods are tracked. We first give the background of designing BNNs and present the typical types of BNNs. The FPGA implementation technologies of BNNs are then reviewed. Detailed comparison with experimental evaluation on typical BNNs and their FPGA implementation is further conducted. Finally, certain interesting directions are also illustrated as future work.