STOP: spatiotemporal orthogonal propagation for weight-threshold-leakage synergistic training of deep spiking neural networks

The prevailing of artificial intelligence-of-things calls for higher energy-efficient edge computing paradigms, such as neuromorphic agents leveraging brain-inspired spiking neural network (SNN) models based on spatiotemporally sparse binary spikes. However, the lack of efficient and high-accuracy d...

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Detaylı Bibliyografya
Asıl Yazarlar:	Haoran Gao, Xichuan Zhou, Yingcheng Lin, Min Tian, Liyuan Liu, Cong Shi
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	IOP Publishing 2025-01-01
Seri Bilgileri:	Neuromorphic Computing and Engineering
Konular:	neuromorphic computing spiking neural network (SNN) backpropagation through time (BPTT) spatiotemporal backpropagation (STBP) synergistic learning
Online Erişim:	https://doi.org/10.1088/2634-4386/ae0a78
Etiketler:	Etiketle Etiket eklenmemiş, İlk siz ekleyin!

STOP: spatiotemporal orthogonal propagation for weight-threshold-leakage synergistic training of deep spiking neural networks

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