基于脉冲神经网络与移动GPU计算的图像分类算法研究与实现

Image classification algorithm based on spiking neural network and mobile GPU computing

计算机视觉旨在通过计算机模拟人的视觉系统,让计算机学会“看”,是人工智能、神经科学研究的一个热点。作为计算机视觉的经典任务,图像分类吸引了越来越多的研究,尤其是基于神经网络的算法在各种分类任务上表现优异。然而,传统浅层人工神经网络特征学习能力不强、生物可解释性不足,而深层神经网络存在过拟合、高功耗的缺点,因此在低功耗环境下具有生物可解释性的图像分类算法研究仍然是一个具有挑战性的任务。为了解决上述问题,结合脉冲神经网络,设计并实现了一种基于Jetson TK1和脉冲神经网络的图像分类算法。研究的主要创新点有:(1)设计了深度脉冲卷积神经网络算法,用于图像分类;(2)实现了基于CUDA改进的脉冲神经网络模型,并部署在Jetson TK1开发环境上。

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.