Efficient sparse spiking auto-encoder for reconstruction, denoising and classification

Auto-encoders are capable of performing input reconstruction, denoising, and classification through an encoder-decoder structure. Spiking Auto-Encoders (SAEs) can utilize asynchronous sparse spikes to improve power efficiency and processing latency on neuromorphic hardware. In our work, we propose a...

Full description

Bibliographic Details

Published in:	Neuromorphic Computing and Engineering
Main Authors:	Ben Walters, Hamid Rahimian Kalatehbali, Zhengyu Cai, Roman Genov, Amirali Amirsoleimani, Jason Eshraghian, Mostafa Rahimi Azghadi
Format:	Article
Language:	English
Published:	IOP Publishing 2024-01-01
Subjects:	SAE STDP spiking neural networks (SNNs) neuromorphic computing
Online Access:	https://doi.org/10.1088/2634-4386/ad5c97