Activated Current Sensing Circuit for Resistive Neuromorphic Networks Recently, resistivebased neural networks have been adopted to build deep learning architectures, where the small area footprint RRAMs (memristors) enables unprecedentedly large neural networks. In this work, we introduce a current sensing circuit and an integrated activation function for resistive neural networks for the first time. This circuit is vital since it is replicated hundreds of times at the outputs of the neurons and thus should be low power and ultracompact. The proposed circuit is designed using TSMC65nm. The circuit is based on the current conveyor principle and a simple inverter to create the required activation function. The obtained response is curvefitted to hyperbolic tangent and sigmoid functions to get the accurate expression for the nonlinear function used to design the training technique of the entire neural network. Finally, the proposed circuit is tested in a fourbit neural network based ADC.

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.