AHAR: Adaptive CNN for Energy-Efficient Human Activity Recognition in Low-Power Edge Devices-Reference-Cited by-同舟云学术

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AHAR: Adaptive CNN for Energy-Efficient Human Activity Recognition in Low-Power Edge Devices

Published:2022-08-01 Issue:15 Volume:9 Page:13041-13051
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Rashid Nafiul¹^ORCID,Demirel Berken Utku¹^ORCID,Abdullah Al Faruque Mohammad¹^ORCID

Affiliation:

1. Department of Electrical Engineering and Computer Science, University of California at Irvine, Irvine, CA, USA

Funder

Graduate Assistance in Areas of National Need (GAANN) Award from the United States Department of Education

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/9839365/09669989.pdf?arnumber=9669989

Reference41 articles.

1. Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks

2. Distilling the knowledge in a neural network;hinton;ArXiv 1503 02531,2015

3. EExNAS: Early-Exit Neural Architecture Search Solutions for Low-Power Wearable Devices

4. Energy-Aware Design Methodology for Myocardial Infarction Detection on Low-Power Wearable Devices

5. Darts: Differentiable architecture search;liu;Arxiv 1806 09055,2018

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