Abstract

Spiking Neural Networks (SNNs) have emerged as an attractive spatio-temporal computing paradigm for complex vision tasks. However, most existing works yield models that require many time steps and do not leverage the inherent temporal dynamics of spiking neural networks, even for sequential tasks. Motivated by this observation, we propose an optimized spiking long short-term memory networks (LSTM) training framework that involves a novel ANN-to-SNN conversion framework, followed by SNN fine-tuning via backpropagation through time (BPTT). In particular, we propose novel activation functions in the source LSTM architecture and convert a judiciously selected subset of them to leaky-integrate-and-fire (LIF) activations with optimal bias shifts. Moreover, we propose a pipelined parallel processing scheme that hides the SNN time steps, significantly improving system latency, especially for long sequences …

Metadata

publication

2023 IEEE/ACM International Symposium on Low Power Electronics and Design …, 2023

year

2023

publication date

2023/8/7

authors

Gourav Datta, Haoqin Deng, Robert Aviles, Zeyu Liu, Peter A Beerel

link

https://ieeexplore.ieee.org/abstract/document/10244298/

conference

2023 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)

pages

1-6

publisher

IEEE