Design and implementation of spiking neural network based on FPGA

Existing software simulations for spiking neural networks usually have the disadvantages of low processing speed and high-power consumption, while the hardware implementations have the disadvantages of high development difficulty and poor flexibility. To explore a reasonable implementation of the spiking neural networks, a novel method is proposed in which the network topology is simulated by the software simulation libraries, and the key computations are handed over to the FPGA forparallel computing to meet the requirements of easydevelopment, high flexibility, fast processing speed, and low power consumption. The main work is as follows: The software library and the OpenCL development library are extended, and the key modules of the software library are reconstructed into the FPGA kernels so that the software library can call the FPGA to execute the computing tasks. The experimental results on image classification of MNIST datasets show that the classification accuracy of the proposed scheme is comparable with that of the software simulation on GPU, and the reference power consumption is reduced by about 63.6% at the cost of a slight reduction in processing efficiency.