Adaptive and hierarchical run-time manager for energy-aware thermal management of embedded systems
- Submitting institution
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University of Southampton
- Unit of assessment
- 12 - Engineering
- Output identifier
- 20808266
- Type
- D - Journal article
- DOI
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10.1145/2834120
- Title of journal
- ACM Transactions on Embedded Computing Systems
- Article number
- 24
- First page
- -
- Volume
- 15
- Issue
- 2
- ISSN
- 1539-9087
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2016
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper used machine learning to manage the thermal lifetime of many-core mobile processors, and was evaluated on real hardware. The paper contributed to the invitation/delivery of keynote talks at ReCoSoc 2019 and PATMOS 2017, an invited talk at the Arm Research Summit 2018, and a tutorial at ESWeek 2018. The research led to a subsequent £1.2M EPSRC project (EP/S030069/1), applying its runtime management approaches to machine learning workloads. Societal impact was achieved through a public engagement activity at Science and Engineering Day 2017 (http://bit.ly/2MkmAVo). The lead author was subsequently appointed as an assistant professor at Drexel, USA (http://bit.ly/2DXzG3R).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -