SRIF: Energy- and Area-Efficient Soft Reset Integrate-and-Fire Neuron for High-Accuracy Spiking Neural Network

In this work, we present an energy- and area-efficient soft reset integrate-and-fire (SRIF) neuron circuit for spiking neural networks (SNNs), designed to improve inference accuracy by retaining the residual membrane potential above the firing threshold. The conventional current-subtraction soft reset neuron introduces significant performance overhead. In contrast, the proposed SRIF neuron employs the charge-sharing mechanism, characterized by its simple structure and minimal energy overhead. The 28-nm CMOS SRIF neuron exhibits an energy consumption of 14.7 fJ per spike and occupies an area of 107 µm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>. Additionally, the soft reset mechanism achieves a 39.5% improvement in inference accuracy compared to the hard reset baseline when benchmarked with VGG-11 network trained on the CIFAR-10 dataset. Moreover, a capacitor-sharing strategy is proposed to improve the utilization efficiency of reset capacitors, providing 1.24x smaller neuron area.