Capacitor-less RRAM-based stochastic neuron for event-based unsupervised learning

Abstract

Stochastic neuron is the key for event-based probabilistic neural networks. Here, for the first time, we propose a stochastic neuron based on Metal-Oxide Resistive Random-Access Memory (RRAM). The RRAM's conducting filament with built-in stochasticity is used to mimic the neuron's membrane capacitor that temporally integrates input spikes. A fully asynchronous and capacitor-less neuron circuit is designed, laid out, and simulated. The output spike train of the neuron is shown to obey the Poisson distribution. Using the 65nm CMOS technology node, the area of the neuron is 14 × 5 μm2, which is one ninth size of an 1pF capacitor. The average power consumption of the neuron is 1.289 μW. Finally, an event-based spiking deep belief network (DBN) is evaluated using our neuron design. The DBN is trained using MNIST database. Simulation results show that the DBN is able to recognize the handwritten digits with 90% accuracy.