A Workload-Aware DVFS Robust to Concurrent Tasks for Mobile Devices

Author:

Lin Chengdong12ORCID,Wang Kun1ORCID,Li Zhenjiang1ORCID,Pu Yu2ORCID

Affiliation:

1. City University of Hong Kong, Hong Kong, China

2. Alibaba DAMO Academy, Shanghai, China

Publisher

ACM

Reference83 articles.

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4. Energy aware scheduling. https://www.kernel.org/doc/html/next/scheduler/sched-energy.html. Energy aware scheduling. https://www.kernel.org/doc/html/next/scheduler/sched-energy.html.

5. Energy consumption issue. https://www.marketsandmarkets.com/Market-Reports/embedded-system-market-98154672.html. Energy consumption issue. https://www.marketsandmarkets.com/Market-Reports/embedded-system-market-98154672.html.

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1. DVFO: Learning-Based DVFS for Energy-Efficient Edge-Cloud Collaborative Inference;IEEE Transactions on Mobile Computing;2024-10

2. Deep Reinforcement Learning-Based Power Management for Chiplet-Based Multicore Systems;IEEE Transactions on Very Large Scale Integration (VLSI) Systems;2024-09

3. Sustainable edge computing with mobile crowd computing: a proof-of-concept with a smart HVAC use case;The Journal of Supercomputing;2024-07-29

4. A Learning-Based and Network-Aware Power Management for Mobile Devices;2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC);2024-07-02

5. Pantheon: Preemptible Multi-DNN Inference on Mobile Edge GPUs;Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services;2024-06-03

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