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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...

Whakaahuatanga katoa

I tiakina i:
Ngā kaituhi matua: Ben Walters, Hamid Rahimian Kalatehbali, Zhengyu Cai, Roman Genov, Amirali Amirsoleimani, Jason Eshraghian, Mostafa Rahimi Azghadi
Hōputu: Artigo
Reo:Inglês
I whakaputaina: IOP Publishing 2024-01-01
Rangatū:Neuromorphic Computing and Engineering
Ngā marau:
SAE
STDP
spiking neural networks (SNNs)
neuromorphic computing
Urunga tuihono:https://doi.org/10.1088/2634-4386/ad5c97
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