An improved spiking network conversion for image classification

Image classification is always an interesting problem due to its practical applications in real life. With a capability of self-learning features, modern Convolution Neural Network (CNN) models can achieve high accuracy on large and complex benchmark datasets. However, due to their high computation costs, the CNN models experience energy consumption problems during training and implementation of the hardware which limits their utilisation in mobile and embedded applications. Recently, the Spiking Neural Network (SNN) has been proposed to overcome drawbacks of the CNN models. Like the biological nervous system, the SNN’s neurons communicate with each other by sending spike trains. A neuron is only calculated when a new input spike arrives. As a result, it turns the networks into an energy-saving mode which is suitable for implementation on hardware devices. To avoid the difficulty of the SNN direct training, an indirect training approach is proposed in this work. A proposed CNN model is firstly trained with the RMSprop algorithm then the optimised weights and bias are mapped to the SNN model converted from the proposed CNN model. Experimental results confirm that our model achieves the best accuracy of 93.5% when compared to state-of-the-art SNN approaches on the Fashion- MNIST dataset.