Spiking convolution engine for spiking convolution neural networks

A promising alternative in artificial vision tasks that considerably re-duces computational cost and power is neuromorphic event-based processing. In this context, we employed a multiconvolution event-based system on a FPGA, inspired by the Leaky Integrate-and-Fire (LIF) neuron, to demonstrate its viabil-ity in implementing a Spiking Convolutional Neural Network (SCNN). In this work, we show that the convolution layers of the LeNet-5 network, trained with MNIST, can be implemented in a spiking manner, and discuss the necessary mod-ifications to the architecture to offer this solution in real-time.