Convolutional Neural Network Construction Method for Embedded FPGAs Oriented Edge Computing

At present, applications and services with high computational consumption migrate gradually from centralized cloud computing center to embedded environment in the network edge. FPGA is widely used in the embedded systems under edge computing because of its flexibility and high efficiency. The conventional FPGA based convolutional neural network construction method has shortcomings, such as long design cycle and small optimization space, which leads to an ineffective exploration of the design space of targeted hardware accelerator, especially in network edge embedded environment. In order to overcome these issues, a high level synthesis based general method for convolutional neural network construction on embedded FPGA oriented edge computing is proposed. The highly reusable accelerator function is designed to construct the optimized convolutional neural network with a lower hardware resource consumption. Scalable design methodology, memory optimization and data flow enhancement are implemented on the accelerator core with HLS design strategy. The convolutional neural network is built on embedded FPGA platforms. The results show the advantage of performance and power when compared with Xeon E5-1620 CPU and GTX K80 GPU, and suitable for edge computing environment.