Abstract

Neuromorphic computing and, in particular, spiking neural networks (SNNs) have become an attractive alternative to deep neural networks for a broad range of signal processing applications, processing static and/or temporal inputs from different sensory modalities, including audio and vision sensors. In this paper, we start with a description of recent advances in algorithmic and optimization innovations to efficiently train and scale low-latency, and energy-efficient spiking neural networks (SNNs) for complex machine learning applications. We then discuss the recent efforts in algorithm-architecture co-design that explores the inherent trade-offs between achieving high energy-efficiency and low latency while still providing high accuracy and trustworthiness. We then describe the underlying hardware that has been developed to leverage such algorithmic innovations in an efficient way. In particular, we describe a …

Metadata

publication

ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and …, 2024

year

2024

publication date

2024/4/14

authors

Souvik Kundu, Rui-Jie Zhu, Akhilesh Jaiswal, Peter A Beerel

link

https://ieeexplore.ieee.org/abstract/document/10445826/

resource_link

https://arxiv.org/pdf/2312.01213

source

ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

pages

13256-13260

publisher

IEEE