Robust Brain-Inspired Computing: On the Reliability of Spiking Neural Network Using Emerging Non-Volatile Synapses
Journal
IEEE International Reliability Physics Symposium Proceedings
Journal Volume
2021-March
Date Issued
2021
Author(s)
Abstract
A spiking neural network (SNN) with non-volatile memory synapses can facilitate an ideal analog approach with low power consumption for intelligent applications. However, reliability issues would arise due to the characteristics of Non-Volatile Memory synapses operating under the limited size of a neural circuit component called membrane capacitor, resulting in low precision MAC. Through a simulation study, we identified several important criteria of the memory synapses that affect performance of the SNN for the membrane capacitor size of 1pF. (1) The required ON-OFF ratio of synapse needs to be >1000 to preserve classification accuracy. (2) Low on-current Ion (<10uA) is preferred for low power. (3) The variation and error of Ion should be lower than +/-10% of mean value. ? 2021 IEEE.
Subjects
Low power electronics; Nonvolatile storage; Reliability; Brain-inspired computing; Classification accuracy; Intelligent applications; Low-power consumption; Membrane capacitors; Non-volatile memory; Spiking neural network(SNN); Spiking neural networks; Neural networks
Type
conference paper