Deep Spiking Convolutional Neural Networks for Programmable Neurosynaptic System Binary neural networks (BNNs)  neural networks with binary 1, 1 weights and activations at runtime, have attracted much attention in recent years, which drastically reduce resource requirement and power consumption with tolerable accuracy loss. While, spiking neural networks (SNNs) are designed as a braininspired computing model, which can be deployed on largescale distributed and eventbased neuromorphic hardware efficiently. To enable a conversion of above two, we develop a variant of BNNs which employ binaryvalued 0, 1 activations and ternaryvalued 1, 0, 1 weights and achieve startoftheart classification accuracy across two standard vision datasets, even better than the previous proposals using BNNs or SNNs. Furtherly, we propose an effective configuration method for network deployment on our designed programmable neurosynaptic system with only one timestep and milliwatt level power consumption.

A digitalized RRAMbased Spiking Neuron Network system with 3bit weight and unsupervised online learning scheme Resistiveswitching Random Access Memory (RRAM) has emerged as a promising candidate for the artificial synaptic in neuromorphic computation circuits due to its similar electronic characteristics with the synaptic and features such as high integration density, nonvolatile retention and supporting matrixvector multiplication. In this paper, a digitalized RRAMbased fullyconnected Spiking Neuron Network (SNN) system with 3bit weight and unsupervised online learning scheme is proposed. It consists of 64 preneurons and 10 postneurons, all the neurons are realized by digital circuits for low area overhead, low power consumption and high accuracy. An unsupervised online learning scheme based on binary STDP protocol is applied to train the synaptic weights. Experiments show that the system can be used to recognize the learned ten handwritten digits efficiently.