Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms

Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms

Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks requires vast computing resources, leadi...

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Bibliographic Details
Main Authors: Evangelos eStromatias, Daniel eNeil, Michael ePfeiffer, Francesco eGalluppi, Steve B Furber, Shih-Chii eLiu
Format: Article
Language:English
Published: Frontiers Media S.A. 2015-07-01
Series:Frontiers in Neuroscience
Subjects:
Deep Belief Networks
spiking neural networks
Noise robustness
SpiNNaker
Neuro-inspired Hardware
bit precison
Online Access:http://journal.frontiersin.org/Journal/10.3389/fnins.2015.00222/full

Internet

http://journal.frontiersin.org/Journal/10.3389/fnins.2015.00222/full

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