EASpiNN: Effective Automated Spiking Neural Network Evaluation on FPGA

Neural networks (NNs) have been widely used in many machine learning algorithms and have been deployed for various industrial applications like image classification, speech recognition, and automated control. Spiking neural network (SNN), known as the third-generation neural network, incorporates timing information in the network and is more biologically plausible [1]. Compared to today's artificial and convolutional neural networks (ANN and CNN) where all neurons in each layer will always be activated and computed, SNN only activates those neurons whose membrane potential exceed the threshold potential [2]. As a result, SNN requires fewer computation resources and less data communication between network layers due to its event-driven nature. Although SNN has been blamed for the relatively lower accuracy, recent studies on converted SNNs have improved its accuracy to a similar level of ANN and CNN for smaller network models like MNIST and CIFAR-10, and have demonstrated the great potential of SNN in future deep learning systems [2].