Fully Integrated Spiking Neural Network with Analog Neurons and RRAM Synapses

This paper presents, to the best of the authors' knowledge, the first complete integration of a Spiking Neural Network, combining analog neurons and Resistive RAM (RRAM)-based synapses. The implemented topology is a perceptron, aimed at performing MNIST classification. An existing framework was tailored for offline learning and weight quantization. The test chip, fabricated in 130nm CMOS, shows well-controlled integration of synaptic currents and no RRAM read disturb issue during inference tasks (at least 750M spikes). The classification accuracy is 84%, with a 3.6 pJ energy dissipation per spike at the synapse and neuron level (up to 5x lower vs. similar chips using formal coding).