Reliability of Event Timing in Silicon Neurons
This addresses the bottleneck of variability in neuromorphic hardware for energy-efficient computing, though it appears incremental by applying biological insights to existing models.
The paper tackled the problem of timing reliability in silicon neurons due to noise and variability, showing that reliable spike transmission can be achieved similarly to biological neurons, with demonstrations including single spike, burst, and network control.
Analog, low-voltage electronics show great promise in producing silicon neurons (SiNs) with unprecedented levels of energy efficiency. Yet, their inherently high susceptibility to process, voltage and temperature (PVT) variations, and noise has long been recognised as a major bottleneck in developing effective neuromorphic solutions. Inspired by spike transmission studies in biophysical, neocortical neurons, we demonstrate that the inherent noise and variability can coexist with reliable spike transmission in analog SiNs, similarly to biological neurons. We illustrate this property on a recent neuromorphic model of a bursting neuron by showcasing three different relevant types of reliable event transmission: single spike transmission, burst transmission, and the on-off control of a half-centre oscillator (HCO) network.