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Abstract
This research presents a systematic methodology for producing accurate power models for single instruction set architecture (ISA) heterogeneous processors. We use the hardware event counters from the processor performance monitoring unit (PMU) to accurately capture the CPU states and ordinary least squares (OLS), assisted by automated event selection algorithms, to compute the power models. Several estimators for single-thread and multi-thread benchmarks are proposed capable of performing power predictions across different frequency levels for one processor as well as between the heterogeneous processors with less than 3% error. The models are compared to related work showing significant improvement in accuracy and good computational efficiency which makes them suitable for run-time deployment.
Original language | English |
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Pages (from-to) | 324-340 |
Number of pages | 17 |
Journal | International Journal of Embedded Systems |
Volume | 12 |
Issue number | 3 |
Early online date | 28 Apr 2020 |
DOIs | |
Publication status | Published - 2020 |
Bibliographical note
Funding Information:This work is supported by ARM Research funding, through an EPSRC iCASE studentship and the University of Bristol and by the EPSRC ENEAC grant number EP/N002539/1.
Publisher Copyright:
Copyright © 2020 Inderscience Enterprises Ltd.
Keywords
- Automated event selection
- Big.LITTLE system-on-chip
- Energy efficiency
- Energy prediction
- Hardware performance events
- Heterogenous processor
- Linear regression
- Ordinary least squares
- Power modelling
Fingerprint
Dive into the research topics of 'Intra and inter-core power modelling for single-ISA heterogeneous processors'. Together they form a unique fingerprint.Projects
- 1 Finished
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ENergy Efficient Adaptive Computing with multi-grain heterogeneous architectures (ENEAC)
Nunez-Yanez, J. L. (Principal Investigator)
5/01/16 → 4/01/20
Project: Research