Design Considerations for Energy-efficient Inference on Edge Devices

Author:

Hanafy Walid A.1,Molom-Ochir Tergel1,Shenoy Rohan1

Affiliation:

1. University of Massachusetts Amherst

Funder

NSF (National Science Foundation)

Publisher

ACM

Cited by 12 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. Energy-efficient neural network training through runtime layer freezing, model quantization, and early stopping;Computer Standards & Interfaces;2025-03

2. Acies-OS: A Content-Centric Platform for Edge AI Twinning and Orchestration;2024 33rd International Conference on Computer Communications and Networks (ICCCN);2024-07-29

3. Energy Time Fairness: Balancing Fair Allocation of Energy and Time for GPU Workloads;Proceedings of the Eighth ACM/IEEE Symposium on Edge Computing;2023-12-06

4. Failure-Resilient ML Inference at the Edge through Graceful Service Degradation;MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM);2023-10-30

5. Green AI Quotient: Assessing Greenness of AI-based software and the way forward;2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE);2023-09-11

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