Conversion-based Approach to Obtain an SNN Construction

Abstract

Spiking Neuron Network (SNN) uses spike sequence for data processing, so it has an excellent characteristic of low power consumption. However, due to the immaturity of learning algorithm, the multiplayer network training has difficulty in convergence. Utilizing the mature learning algorithm and fast training speed of the back-propagation network, this paper proposes a method to converse the Convolutional Neural Network (CNN) to the SNN. First, the adjustment strategy for CNN is introduced. Then after training, the weight parameters in the model are extracted, which is the corresponding synaptic weight in the layer of the SNN. Finally, a new threshold-setting algorithm based on feedback is proposed to solve the critical problem of the threshold setting of neurons in the SNN. We evaluate our method on the CIFAR-10 datasets released by Hinton’s team. The experimental results show that the image classification accuracy of the SNN is more than 98% of that of CNN, and the theoretical value of power consumption per second is 3.9[Formula: see text]mW.