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Feedforward neural networks on massively parallel architectures
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-6289-1521
2020 (English)In: Hardware Architectures for Deep Learning, Institution of Engineering and Technology , 2020, p. 53-76Chapter in book (Other academic)
Abstract [en]

In this chapter, we present ClosNN, a specialized NoC for NNs based on the well-known Clos topology. Clos is perhaps the most popular Multistage Interconnection Network (MIN) topology. Clos is used commonly as a base of switching infrastructures in various commercial telecommunication and network routers and switches.

Place, publisher, year, edition, pages
Institution of Engineering and Technology , 2020. p. 53-76
Keywords [en]
ClosNN, Feedforward neural nets, Feedforward neural network, Massively parallel architectures, MIN topology, Multistage interconnection network topology, Network routers, Network-on-chip, NoC, Parallel architectures, Switches, Switching infrastructures
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-62548DOI: 10.1049/PBCS055E_ch3Scopus ID: 2-s2.0-85153639577ISBN: 9781785617683 (print)OAI: oai:DiVA.org:mdh-62548DiVA, id: diva2:1756355
Note

Book chapter; Export Date: 11 May 2023; Cited By: 0

Available from: 2023-05-11 Created: 2023-05-11 Last updated: 2023-05-11Bibliographically approved

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Daneshtalab, Masoud

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