Exploring Spiking Neural Networks (SNN) for Low Size, Weight, and Power (SWaP) Benefits

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dc.contributor.authorBihl, Trevor
dc.contributor.authorFarr, Patrick
dc.contributor.authorDi Caterina, Gaetano
dc.contributor.authorVicente-Sola, Alex
dc.contributor.authorManna, Davide
dc.contributor.authorKirkland, Paul
dc.contributor.authorLiu, Jundong
dc.contributor.authorCombs, Kara
dc.date.accessioned2023-12-26T18:54:36Z
dc.date.available2023-12-26T18:54:36Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2023.908
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.otherc095aff2-b778-429e-8344-53d6edc11a32
dc.identifier.urihttps://hdl.handle.net/10125/107294
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectIntelligent Edge Computing in Pervasive Environments
dc.subjectdeep learning
dc.subjectedge processing
dc.subjectneural networks
dc.subjectneuromorphics
dc.subjectswap
dc.titleExploring Spiking Neural Networks (SNN) for Low Size, Weight, and Power (SWaP) Benefits
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractSize, Weight, and Power (SWaP) concerns are growing as artificial intelligence (AI) use spreads in edge applications. AI algorithms, such as artificial neural networks (ANNs), have revolutionized many fields, e.g. computer vision (CV), but at a large computational/power burden. Biological intelligence is notably more computationally efficient. Neuromorphic edge processors and spiking neural networks (SNNs) aim to follow biology closer with spike-based operations resulting in sparsity and lower-SWaP operations than traditional ANNs with SNNs only “firing/spiking” when needed. Understanding the trade space of SWaP when embracing neuromorphic computing has not been studied heavily. To addresses this, we present a repeatable and scalable apples-to-apples comparison of traditional ANNs and SNNs for edge processing with demonstration on both classical and neuromorphic edge hardware. Results show that SNNs combined with neuromorphic hardware can provide comparable accuracy for CV to ANNs at 1/10th the power.
dcterms.extent10 pages
prism.startingpage7561

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Intelligent Edge Computing in Pervasive Environments