Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review-Reference-Cited by-同舟云学术

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Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review

Published:2023-01 Issue:1 Volume:111 Page:42-91
ISSN:0018-9219
Container-title:Proceedings of the IEEE
language:
Short-container-title:Proc. IEEE

Author:

Shuvo Md. Maruf Hossain¹^ORCID,Islam Syed Kamrul¹^ORCID,Cheng Jianlin²,Morshed Bashir I.³^ORCID

Affiliation:

1. Department of Electrical Engineering and Computer Science, Analog/Mixed Signal VLSI and Devices Laboratory (AVDL), University of Missouri, Columbia, MO, USA

2. Department of Electrical Engineering and Computer Science, Bioinformatics and Machine Learning Laboratory (BML), University of Missouri, Columbia, MO, USA

3. Department of Computer Science, Cyber Physical Systems (CPS) Laboratory, Texas Tech University, Lubbock, TX, USA

Funder

College of Engineering, University of Missouri, Columbia, MO, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/5/10015202/09985008.pdf?arnumber=9985008

Reference424 articles.

1. A CGRA based Neural Network Inference Engine for Deep Reinforcement Learning

2. Densely Connected Convolutional Networks

3. High performance deep neural network on low cost mobile GPU

4. A Configurable Cloud-Scale DNN Processor for Real-Time AI

5. Xception: Deep Learning with Depthwise Separable Convolutions

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