Neurons With Captive Synaptic Devices for Temperature Robust Spiking Neural Networks

Synaptic devices store the synaptic weight in spiking neural networks (SNNs). However, because synaptic devices are based on memory cells, their synaptic weights are vulnerable to temperature variations, which significantly degrade network accuracy. To implement temperature-robust asynchronous SNNs, neurons with captive synaptic devices (CSD neurons) are proposed in this study. Captive synaptic devices that mimic the temperature characteristics of synaptic devices are located parallel to the integration capacitors. By using the captive synaptic devices for the membrane potential reset, the CSD neurons are inherently equipped to counteract temperature-induced variations. The validity of the CSD neurons was experimentally confirmed using two types of synaptic devices: poly Si-channel charge-trap flash synaptic devices and resistive random access memory devices. When applied to the street view house numbers (SVHN) dataset, the SNNs with CSD neurons successfully maintained a constant inference accuracy value (95.00 %) from 300 to 360 K, even if the output current of the synaptic devices doubled.