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dc.contributor.authorNarduzzi, Simon
dc.contributor.authorFavre, Dorvan
dc.contributor.authorNuria, Pazos
dc.contributor.authorDunbar, L. Andrea
dc.date.accessioned2022-10-28T09:20:00Z
dc.date.available2022-10-28T09:20:00Z
dc.date.issued2022-06
dc.identifier.citationChapter 10, pp. 129-140, in "Industrial Artificial Intelligence Technologies and Applications", by River Publishers Series in Communications and Networkingen_US
dc.identifier.isbn9788770227919
dc.identifier.urihttps://yoda.csem.ch/handle/20.500.12839/1055
dc.description.abstractThe rapid development of embedded technologies in recent decades has led to the advent of dedicated inference platforms for deep learning. However, unlike development libraries for the algorithms, hardware deployment is highly fragmented in both technology, tools, and usability. Moreover, emerging paradigms such as spiking neural networks do not use the same prediction process, making the comparison between platforms difficult. In this paper, we deploy a convolutional neural network model on different platforms comprising microcontrollers with and without deep learning accelerators and an event-based accelerator and compare their performance. We also report the perceived effort of deployment for each platform.en_US
dc.language.isoenen_US
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectneuromorphic computingen_US
dc.subjectIoTen_US
dc.subjectKendryteen_US
dc.subjectDynapCNNen_US
dc.subjectSTM32en_US
dc.subjectperformanceen_US
dc.subjectComparisonen_US
dc.subjectBenchmarken_US
dc.titleDeploying a Convolutional Neural Network on Edge MCU and Neuromorphic Hardware Platformsen_US
dc.typeBook chapteren_US
dc.type.csemdivisionsDiv-Men_US
dc.type.csemresearchareasData & AIen_US
dc.type.csemresearchareasASICs for the Edgeen_US
dc.type.csemresearchareasIoT & Visionen_US
dc.identifier.urlhttps://www.riverpublishers.com/research_details.php?book_id=1024
dc.identifier.doihttps://doi.org/10.13052/rp-9788770227902


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CC0 1.0 Universal
Except where otherwise noted, this item's license is described as CC0 1.0 Universal