IEEE Transactions on Applied Superconductivity, volume 34, issue 3, pages 1-6

An On-Chip Trainable Neuron Circuit for SFQ-Based Spiking Neural Networks

Beyza Zeynep Ucpinar ¹

Mustafa Altay Karamuftuogl ¹

Sasan Razmkhah ¹

M. Pedram ¹

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Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA |

Publication type: Journal Article

Publication date: 2024-05-01

Institute of Electrical and Electronics Engineers (IEEE)

Journal: IEEE Transactions on Applied Superconductivity

scimago Q2

SJR: 0.500

CiteScore: 3.5

Impact factor: 1.7

ISSN: 10518223, 15582515, 23787074

DOI: 10.1109/tasc.2024.3359164

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Electronic, Optical and Magnetic Materials

Condensed Matter Physics

Electrical and Electronic Engineering

Found

	1 2
Superconductor Science and Technology	Superconductor Science and Technology, 2, 50% Superconductor Science and Technology 2 publications, 50%
Mesoscience and Nanotechnology	Mesoscience and Nanotechnology, 1, 25% Mesoscience and Nanotechnology 1 publication, 25%
	1 2

	1 2
IOP Publishing	IOP Publishing, 2, 50% IOP Publishing 2 publications, 50%
Institute of Electrical and Electronics Engineers (IEEE)	Institute of Electrical and Electronics Engineers (IEEE), 1, 25% Institute of Electrical and Electronics Engineers (IEEE) 1 publication, 25%
Treatise	Treatise, 1, 25% Treatise 1 publication, 25%
	1 2

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Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Journal

IEEE Transactions on Applied Superconductivity

scimago Q2

SJR

0.500

CiteScore

3.5

Impact factor

1.7

ISSN

10518223 (Print)

15582515 (Electronic)

23787074 (Electronic)

An On-Chip Trainable Neuron Circuit for SFQ-Based Spiking Neural Networks

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