Abstract

Spiking neural networks (SNNs), that operate via binary spikes distributed over time, have emerged as a promising energy efficient ML paradigm for resource-constrained devices. However, the current state-of-the-art (SOTA) SNNs require multiple time steps for acceptable inference accuracy, increasing spiking activity and, consequently, energy consumption. SOTA training strategies for SNNs involve conversion from a non-spiking deep neural network (DNN). In this paper, we determine that SOTA conversion strategies cannot yield ultra low latency because they incorrectly assume that the DNN and SNN pre-activation values are uniformly distributed. We propose a new training algorithm that accurately captures these distributions, minimizing the error between the DNN and converted SNN. The resulting SNNs have ultra low latency and high activation sparsity, yielding significant improvements in compute …

Date

2022

Authors

Gourav Datta, Peter A Beerel

Conference

2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)

Pages

718-723

Publisher

IEEE