Quantized Magnetic Domain Wall Synapse for Efficient Deep Neural Networks-Reference-Cited by-同舟云学术

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Quantized Magnetic Domain Wall Synapse for Efficient Deep Neural Networks

Published:2024 Issue: Volume: Page:1-10
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Dhull Seema¹^ORCID,Misba Walid Al²^ORCID,Nisar Arshid¹^ORCID,Atulasimha Jayasimha³^ORCID,Kaushik Brajesh Kumar¹^ORCID

Affiliation:

1. Department of Electronics and Communication Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

2. Department of Mechanical and Nuclear Engineering, Virginia Commonwealth University, Richmond, VA, USA

3. Department of Mechanical and Nuclear Engineering and the Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA, USA

Funder

Science and Engineering Research Board, Department of Science and Technology, Government of India

Virginia State’s Commonwealth Cyber Initiative

Virginia Commonwealth University’s Breakthrough through the Energy Efficient Neurocomputer

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/10467190.pdf?arnumber=10467190

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Quantized non-volatile nanomagnetic domain wall synapse based autoencoder for efficient unsupervised network anomaly detection;Neuromorphic Computing and Engineering;2024-06-01

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