Implementation and Applications of Neural Networks Based on FPGA

Deep neural networks have been crucial in several recent developments in artificial intelligence and big data technology, including natural language processing, speech recognition, and computer vision. Given the numerous layers of deep neural network models, the computational complexity, and a large number of parameters, the performance of hardware such as computing power, memory bandwidth, and data storage is highly demanding. Due to their high parallelism, low power consumption, and reconfigurability, Field Programmable Gate Arrays (FPGAs), which are programmable logic devices, are frequently used as an alternative to CPUs and GPUs. Their combination with neural networks has become a current research hotspot in the field of artificial intelligence. This paper briefly describes firstly the current mainstream neural network models and the design ideas of FPGAs. Next, the applications of FPGA-based neural networks are introduced. Finally, this paper concludes with a summary and outlook on the development of FPGA-based applications for deep neural networks.