Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype

Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype

The memory requirement of deep learning algorithms is considered incompatible with the memory restriction of energy-efficient hardware. A low memory footprint can be achieved by pruning obsolete connections or reducing the precision of connection strengths after the network has been trained. Yet, th...

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Bibliographic Details
Main Authors: Chen Liu, Guillaume Bellec, Bernhard Vogginger, David Kappel, Johannes Partzsch, Felix Neumärker, Sebastian Höppner, Wolfgang Maass, Steve B. Furber, Robert Legenstein, Christian G. Mayr
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-11-01
Series:Frontiers in Neuroscience
Subjects:
deep rewiring
pruning
sparsity
SpiNNaker
memory footprint
parallelism
Online Access:https://www.frontiersin.org/article/10.3389/fnins.2018.00840/full

Internet

https://www.frontiersin.org/article/10.3389/fnins.2018.00840/full

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