A neuromorphic hardware design of a spiking convolutional neural network

In recent years, neural network technology has developed rapidly and reached a level comparable to human recognition in the field of image recognition. Traditional convolutional neural networks usually require GPU with high energy consumption in the training and recognition stage, so they cannot be applied to mobile applications requiring small and low-power devices. This paper presents a neuromorphic hardware architecture of spiking convolutional neural networks for recognizing handwriting numbers. Because the neuromorphic only operates when there is a spike input, it can achieve a low power consumption accordingly. When recognizing the MNIST data set, the accuracy of the traditional convolution neural network is 99.0%, whereas the accuracy of the neuromorphic hardware is 98.46%. Therefore, the neuromorphic hardware achieved a comparable recognition accuracy while lowering down the power consumption greatly compared to CNN with a similar hardware architecture.