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...

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Egile Nagusiak:	Ben Walters, Hamid Rahimian Kalatehbali, Zhengyu Cai, Roman Genov, Amirali Amirsoleimani, Jason Eshraghian, Mostafa Rahimi Azghadi
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	IOP Publishing 2024-01-01
Saila:	Neuromorphic Computing and Engineering
Gaiak:	SAE STDP spiking neural networks (SNNs) neuromorphic computing
Sarrera elektronikoa:	https://doi.org/10.1088/2634-4386/ad5c97
Etiketak:	Etiketa erantsi Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!

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

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