Accelerating Deep Learning Inference via Model Parallelism and Partial Computation Offloading-Reference-Cited by-同舟云学术

Accelerating Deep Learning Inference via Model Parallelism and Partial Computation Offloading

Published:2023-02-01 Issue:2 Volume:34 Page:475-488
ISSN:1045-9219
Container-title:IEEE Transactions on Parallel and Distributed Systems
language:
Short-container-title:IEEE Trans. Parallel Distrib. Syst.

Author:

Zhou Huan¹^ORCID,Li Mingze¹^ORCID,Wang Ning²^ORCID,Min Geyong³^ORCID,Wu Jie⁴^ORCID

Affiliation:

1. Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, College of Computer and Information Technology, China Three Gorges University, Yichang, Hubei, China

2. Department of Computer Science, Rowan University, Glassboro, NJ, USA

3. Department of Computer Science, University of Exeter, Exeter, U.K.

4. Center for Networked Computing, Temple University, Philadelphia, PA, USA

Funder

National Natural Science Foundation of China

EU Horizon 2020 Research and Innovation Programme

Marie Sklodowska-Curie

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Hardware and Architecture,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/71/9996310/09953583.pdf?arnumber=9953583

Reference50 articles.

1. Dynamic Adaptive DNN Surgery for Inference Acceleration on the Edge

2. Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing

3. Accelerating DNN Training in Wireless Federated Edge Learning Systems

4. EasiEdge: A Novel Global Deep Neural Networks Pruning Method for Efficient Edge Computing

5. MCDNN

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