Ultra-Low-Power Compact Neuron Circuit with Tunable Spiking Frequency and High Robustness in 22 nm FDSOI

Abstract

Recent years have seen an increasing popularity in the development of brain-inspired neuromorphic hardware for neural computing systems. However, implementing very large scale simulations of neural networks in hardware is still an open challenge in terms of power efficiency, compactness, and biophysical resemblance. In an effort to design biologically plausible spiking neuron circuits while restricting power consumption, we propose a new subthreshold Leaky Integrate-and-Fire (LIF) neuron circuit designed using 22 nm FDSOI technology. In this circuit, problems of large leakage currents and device mismatch are effectively reduced by deploying the back-gate terminal of FDSOI technology for a tunable design. The proposed neuron is able to operate in two spiking frequency modes with tunable bias parameter setting of key transistors, and it results in complex firing behaviors, such as adaptation, chattering, and bursting, through varying bias voltages. We present circuit post-layout simulation results and demonstrate the biologically plausible neural dynamics. Compared with published state-of-the-art neuron circuits, the circuit dissipates ultra-low energy per spike, on the order of femtojoules per spike, at firing rates ranging from 30 Hz to 1 kHz. Furthermore, the circuit is proven to maintain a good robustness over process variation and Monte Carlo analysis, with relative error 3.02% at a firing rate of approximately 67.1 Hz.