Computer vision is designed to simulate human visual systems through machines, which is a hot spot in artificial intelligence and neuroscience research. As a classical task of computer vision, image classification has attracted more and more researches, especially the image classification algorithms based on neural networks perform well on various classification tasks. However, the traditional shallow artificial neural networks have weak feature learning ability and insufficient bio-interpretability, while the deep neural networks have the disadvantages of over-fitting and high power consumption. Therefore, the bio-interpretable classification algorithm in low power environments is still a challenging task. In order to solve the above problems, a classification method based on spiking neural network in Jetson TK1 development environment is designed. The main innovations of the research are as follows:
(1) Designing a spiking convolution neural network for image classification;
(2) Implementing the improved spiking neural network based on CUDA and deploying it in Jetson TK1.