Research on Hardware Acceleration of Traffic Sign Recognition Based on Spiking Neural Network and FPGA Platform
Research on Hardware Acceleration of Traffic Sign Recognition Based on Spiking Neural Network and FPGA Platform
Abstract:
Most of the existing methods for traffic sign recognition exploited deep learning technology such as convolutional neural networks (CNNs) to achieve a breakthrough in detection accuracy; however, due to the large number of CNN’s parameters, there are problems in practical applications such as high power consumption, large calculation, and slow speed. Compared with CNN, a spiking neural network (SNN) can effectively simulate the information processing mechanism of the biological brain, with stronger parallel processing capability, better sparsity, and real-time performance. Thus, we design and realize a novel traffic sign recognition system (called SNN on FPGA-traffic sign recognition system (SFPGA-TSRS)) based on spiking CNN (SCNN) and FPGA platform. Specifically, to improve the recognition accuracy, a traffic sign recognition model spatial attention SCNN (SA-SCNN) is proposed by combining LIF/IF neurons based SCNN with SA mechanism; and to accelerate the model learning, an input coding module is implemented with high performance, and an neuromorphic chip is designed as the hardware part of the recognition model. The experiments show that compared with other methods, the proposed SFPGA-TSRS can efficiently support the design of SCNN models, with a higher recognition accuracy of 99.22%, a faster frame rate of 66.38 frames per second (FPS), and lower power consumption of 1.423 W on the GTSRB dataset.
