An Energy-Efficient FPGA-Based Embedded System for CNN Application

To satisfy the demand of mobile computing and low-power application scenes, we propose a general reconfigurable embedded system design of convolution neural networks (CNN) based on FPGA and soft-core CPU. The basic computing modules are located in the hardware circuits, including convolution, pooling and active layers. Several controlling logic and serial data processing units are executed in the soft-core CPU. Based on the cooperation and interaction between hardware and software, the system is able to accomplish image identification and other CNN works with high speed and low power. As a case study to demonstrate the system's performance and validity, experiments with CNN project are implemented on an Altera Cyclone-IV FPGA board. Our system achieves a peak performance of 59.52GOPS under 120MHz working frequency, with the power of 1.35 W, achieving the power efficiency of 44.1GOPS/W.