Research of Heterogeneous Acceleration Optimization of Convolutional Neural Network Algorithm for Unmanned Vehicle Based on FPGA

Convolutional neural network algorithm has been widely used in unmanned 3D perception and object detection, and FPGA has been more widely used in implementing convolutional neural network with its ultra-low power and ultra-high performance. In this paper, the heterogeneous computing method based on FPGA and various optimization methods are adopted to design the heterogeneous computing structure that meets the performance requirements of the unmanned vehicle algorithm, as well as the specific methods and optimization strategies for heterogeneous implementation. The results show that by adopting the method of heterogeneous optimization design, compared with the CPU calculation method, both the power consumption and the speed have been greatly improved, meeting the requirements of the data processing speed of the unmanned vehicle.