Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks

Hardware-Efficient On-line Learning through Pipelined Truncated-Error Backpropagation in Binary-State Networks

Artificial neural networks (ANNs) trained using backpropagation are powerful learning architectures that have achieved state-of-the-art performance in various benchmarks. Significant effort has been devoted to developing custom silicon devices to accelerate inference in ANNs. Accelerating the traini...

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Bibliographic Details
Main Authors: Hesham Mostafa, Bruno Pedroni, Sadique Sheik, Gert Cauwenberghs
Format: Article
Language:English
Published: Frontiers Media S.A. 2017-09-01
Series:Frontiers in Neuroscience
Subjects:
binary neural networks
pipelined backpropagation
hardware accelerators
supervised learning
online learning
Online Access:http://journal.frontiersin.org/article/10.3389/fnins.2017.00496/full

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http://journal.frontiersin.org/article/10.3389/fnins.2017.00496/full

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