A Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware

Artificial neural networks (ANNs), like convolutional neural networks (CNNs), have achieved the state-of-the-art results for many machine learning tasks. However, inference with large-scale full-precision CNNs must cause substantial energy consumption and memory occupation, which seriously hinders t...

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Detalles Bibliográficos
Autores principales:	Chenglong Zou, Xiaoxin Cui, Yisong Kuang, Kefei Liu, Yuan Wang, Xinan Wang, Ru Huang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	convolutional neural network spiking neural network network quantization network conversion neuromorphic hardware network mapping Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/6e4eedc885474c579ffec00f6a476c8a
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A Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware

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