1/f noise in amorphous Sb2Te3 for energy-efficient stochastic synapses in neuromorphic computing

Abstract

Recent studies on neuromorphic computing have used stochastic synapses to implement power-efficient stochastic computing inspired by unreliable connections between neurons, such as the blank-out noise in the Synaptic Sampling Machine. In this paper, we propose to generate stochasticity by exploiting intrinsic noise in phase change memory (PCM) as a synaptic device, negating additional stochastic devices and circuits that deteriorate the synaptic footprints and power consumption. As existing models are limited to demonstrating the spectral density of noise, we devised a new model based on two-level state theory with a cutoff frequency, resulting in accurate quantification of the finite normalized variance of current () of PCM and its dependence on the volume of the cell and the frequency range in which the measurement is taken. We experimentally verified our model by measuring noise of a phase change bridge cell with an as-deposited amorphous Sb2Te3 as the phase change material. We further analyzed whether the noise in PCM can implement a restricted Boltzmann machine (RBM), in which stochasticity plays a key role, to allow efficient neuromorphic computing. We devised and simulated a spiking neural network (SNN)-based RBM system based on a 832 × 832 PCM synapse array with the intrinsic noise model. As we modeled the normalized synaptic noise with a normal distribution, and optimal standard deviation between 0.01 and 0.05, the on-chip learning and inference test result showed comparable MNIST accuracy and ∼60 times larger estimated energy efficiency than that of the SNN-based RBM, the stochasticity of which is implemented with power-consuming random walk neuron circuits, demonstrating that the intrinsic noise of PCM is not a nuisance but an asset to implement efficient neuromorphic computing systems.