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Energy Transparency for Deeply Embedded Programs

Authors:

Kyriakos Georgiou,

Steve Kerrison,

Zbigniew Chamski,

Kerstin EderAuthors Info & Claims

ACM Transactions on Architecture and Code Optimization (TACO), Volume 14, Issue 1

Article No.: 8, Pages 1 - 26

https://doi.org/10.1145/3046679

Published: 21 March 2017 Publication History

Abstract

Energy transparency is a concept that makes a program’s energy consumption visible, from hardware up to software, through the different system layers. Such transparency can enable energy optimizations at each layer and between layers, as well as help both programmers and operating systems make energy-aware decisions. In this article, we focus on deeply embedded devices, typically used for Internet of Things (IoT) applications, and demonstrate how to enable energy transparency through existing static resource analysis (SRA) techniques and a new target-agnostic profiling technique, without hardware energy measurements. Our novel mapping technique enables software energy consumption estimations at a higher level than the Instruction Set Architecture (ISA), namely the LLVM intermediate representation (IR) level, and therefore introduces energy transparency directly to the LLVM optimizer. We apply our energy estimation techniques to a comprehensive set of benchmarks, including single- and multithreaded embedded programs from two commonly used concurrency patterns: task farms and pipelines. Using SRA, our LLVM IR results demonstrate a high accuracy with a deviation in the range of 1% from the ISA SRA. Our profiling technique captures the actual energy consumption at the LLVM IR level with an average error of 3%.

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Cited By

Castrillon JDesnos KGoens AMenard C(2024)Dataflow Models of Computation for Programming Heterogeneous MulticoresHandbook of Computer Architecture10.1007/978-981-97-9314-3_45(1107-1146)Online publication date: 21-Dec-2024
https://doi.org/10.1007/978-981-97-9314-3_45
Castrillon JDesnos KGoens AMenard C(2023)Dataflow Models of Computation for Programming Heterogeneous MulticoresHandbook of Computer Architecture10.1007/978-981-15-6401-7_45-2(1-40)Online publication date: 28-Sep-2023
https://doi.org/10.1007/978-981-15-6401-7_45-2
Nikov KGeorgiou KChamski ZEder KNunez-Yanez J(2022)Accurate Energy Modelling on the Cortex-M0 Processor for Profiling and Static Analysis2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS)10.1109/ICECS202256217.2022.9971086(1-4)Online publication date: 24-Oct-2022
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Index Terms

Energy Transparency for Deeply Embedded Programs
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics

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cover image ACM Transactions on Architecture and Code Optimization

ACM Transactions on Architecture and Code Optimization Volume 14, Issue 1

March 2017

258 pages

ISSN:1544-3566

EISSN:1544-3973

DOI:10.1145/3058793

Editor:
Koen De Bosschere
Ghent University

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Copyright © 2017 ACM.

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Publication History

Published: 21 March 2017

Accepted: 01 January 2017

Revised: 01 December 2016

Received: 01 September 2016

Published in TACO Volume 14, Issue 1

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ENTRA: Whole-Systems Energy Transparency
ICT-Energy
ARTEMIS Joint Undertaking
European Commission
European Union Seventh Framework Programme (FP7/2007-2013)

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https://doi.org/10.1007/978-981-97-9314-3_45
Castrillon JDesnos KGoens AMenard C(2023)Dataflow Models of Computation for Programming Heterogeneous MulticoresHandbook of Computer Architecture10.1007/978-981-15-6401-7_45-2(1-40)Online publication date: 28-Sep-2023
https://doi.org/10.1007/978-981-15-6401-7_45-2
Nikov KGeorgiou KChamski ZEder KNunez-Yanez J(2022)Accurate Energy Modelling on the Cortex-M0 Processor for Profiling and Static Analysis2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS)10.1109/ICECS202256217.2022.9971086(1-4)Online publication date: 24-Oct-2022
https://doi.org/10.1109/ICECS202256217.2022.9971086
Castrillon JDesnos KGoens AMenard C(2022)Dataflow Models of Computation for Programming Heterogeneous MulticoresHandbook of Computer Architecture10.1007/978-981-15-6401-7_45-1(1-40)Online publication date: 28-Jan-2022
https://doi.org/10.1007/978-981-15-6401-7_45-1
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