Accurate Energy Modelling on the Cortex-M0 Processor for Profiling and Static Analysis

Kris Nikov; Kyriakos Georgiou; Zbigniew Chamski; Kerstin Eder; Jose Nunez-Yanez

doi:10.1109/ICECS202256217.2022.9971086

Accurate Energy Modelling on the Cortex-M0 Processor for Profiling and Static Analysis

Kris Nikov, Kyriakos Georgiou, Zbigniew Chamski, Kerstin Eder, Jose Nunez-Yanez

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

5 Citations (Scopus)

116 Downloads (Pure)

Abstract

Energy modelling can enable energy-aware software development and assist the developer in meeting an application's energy budget. Although many energy models for embedded processors exist, most do not account for processor-specific configurations, neither are they suitable for static energy consumption estimation. This paper introduces a set of comprehensive energy models for Arm's Cortex-M0 processor, ready to support energy-aware development of edge computing applications using either profiling- or static-analysis-based energy consumption estimation. We use a commercially representative physical platform together with a custom modified Instruction Set Simulator to obtain the physical data and system state markers used to generate the models. The models account for different processor configurations which all have a significant impact on the execution time and energy consumption of edge computing applications. Unlike existing works, which target a very limited set of applications, all developed models are generated and validated using a very wide range of benchmarks from a variety of emerging IoT application areas, including machine learning and have a prediction error of less than 5%.

Original language	English
Title of host publication	2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	4
ISBN (Electronic)	978-1-6654-8823-5
ISBN (Print)	978-1-6654-8824-2
DOIs	https://doi.org/10.1109/ICECS202256217.2022.9971086
Publication status	Published - 30 Jan 2023
Event	29th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2022 - Glasgow, United Kingdom Duration: 24 Oct 2022 → 26 Oct 2022

Publication series

Name	ICECS 2022 - 29th IEEE International Conference on Electronics, Circuits and Systems, Proceedings

Conference

Conference	29th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2022
Country/Territory	United Kingdom
City	Glasgow
Period	24/10/22 → 26/10/22

Bibliographical note

Funding Information:
This research is supported by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 779882, TeamPlay (Time, Energy and security Analysis for Multi/Many-core heterogeneous PLAtforms).

Funding Information:
This research is supported by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 779882, TeamPlay (Time, Energy and security Analysis for Multi/Many-core heterogeneous PLAtforms).

Publisher Copyright:
© 2022 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Arm Cortex-M0
edge computing
embedded systems
energy modelling
IoT

Access to Document

10.1109/ICECS202256217.2022.9971086

Full-text PDF (accepted manuscript)
This is the accepted author manuscript (AAM). The final published version (version of record) is available online via IEEE at https://doi.org/10.1109/ICECS202256217.2022.9971086. Please refer to any applicable terms of use of the publisher.
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Nikov, K., Georgiou, K., Chamski, Z., Eder, K., & Nunez-Yanez, J. (2023). Accurate Energy Modelling on the Cortex-M0 Processor for Profiling and Static Analysis. In 2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS) (ICECS 2022 - 29th IEEE International Conference on Electronics, Circuits and Systems, Proceedings). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICECS202256217.2022.9971086

Nikov, Kris ; Georgiou, Kyriakos ; Chamski, Zbigniew et al. / Accurate Energy Modelling on the Cortex-M0 Processor for Profiling and Static Analysis. 2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS). Institute of Electrical and Electronics Engineers (IEEE), 2023. (ICECS 2022 - 29th IEEE International Conference on Electronics, Circuits and Systems, Proceedings).

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title = "Accurate Energy Modelling on the Cortex-M0 Processor for Profiling and Static Analysis",

abstract = "Energy modelling can enable energy-aware software development and assist the developer in meeting an application's energy budget. Although many energy models for embedded processors exist, most do not account for processor-specific configurations, neither are they suitable for static energy consumption estimation. This paper introduces a set of comprehensive energy models for Arm's Cortex-M0 processor, ready to support energy-aware development of edge computing applications using either profiling- or static-analysis-based energy consumption estimation. We use a commercially representative physical platform together with a custom modified Instruction Set Simulator to obtain the physical data and system state markers used to generate the models. The models account for different processor configurations which all have a significant impact on the execution time and energy consumption of edge computing applications. Unlike existing works, which target a very limited set of applications, all developed models are generated and validated using a very wide range of benchmarks from a variety of emerging IoT application areas, including machine learning and have a prediction error of less than 5\%.",

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note = "Funding Information: This research is supported by the European Union{\textquoteright}s Horizon 2020 Research and Innovation Programme under grant agreement No. 779882, TeamPlay (Time, Energy and security Analysis for Multi/Many-core heterogeneous PLAtforms). Funding Information: This research is supported by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 779882, TeamPlay (Time, Energy and security Analysis for Multi/Many-core heterogeneous PLAtforms). Publisher Copyright: {\textcopyright} 2022 IEEE.; 29th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2022 ; Conference date: 24-10-2022 Through 26-10-2022",

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Nikov, K, Georgiou, K, Chamski, Z, Eder, K & Nunez-Yanez, J 2023, Accurate Energy Modelling on the Cortex-M0 Processor for Profiling and Static Analysis. in 2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS). ICECS 2022 - 29th IEEE International Conference on Electronics, Circuits and Systems, Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 29th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2022, Glasgow, United Kingdom, 24/10/22. https://doi.org/10.1109/ICECS202256217.2022.9971086

Accurate Energy Modelling on the Cortex-M0 Processor for Profiling and Static Analysis. / Nikov, Kris; Georgiou, Kyriakos; Chamski, Zbigniew et al.
2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS). Institute of Electrical and Electronics Engineers (IEEE), 2023. (ICECS 2022 - 29th IEEE International Conference on Electronics, Circuits and Systems, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Accurate Energy Modelling on the Cortex-M0 Processor for Profiling and Static Analysis

AU - Nikov, Kris

AU - Georgiou, Kyriakos

AU - Chamski, Zbigniew

AU - Eder, Kerstin

AU - Nunez-Yanez, Jose

N1 - Funding Information: This research is supported by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 779882, TeamPlay (Time, Energy and security Analysis for Multi/Many-core heterogeneous PLAtforms). Funding Information: This research is supported by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 779882, TeamPlay (Time, Energy and security Analysis for Multi/Many-core heterogeneous PLAtforms). Publisher Copyright: © 2022 IEEE.

PY - 2023/1/30

Y1 - 2023/1/30

N2 - Energy modelling can enable energy-aware software development and assist the developer in meeting an application's energy budget. Although many energy models for embedded processors exist, most do not account for processor-specific configurations, neither are they suitable for static energy consumption estimation. This paper introduces a set of comprehensive energy models for Arm's Cortex-M0 processor, ready to support energy-aware development of edge computing applications using either profiling- or static-analysis-based energy consumption estimation. We use a commercially representative physical platform together with a custom modified Instruction Set Simulator to obtain the physical data and system state markers used to generate the models. The models account for different processor configurations which all have a significant impact on the execution time and energy consumption of edge computing applications. Unlike existing works, which target a very limited set of applications, all developed models are generated and validated using a very wide range of benchmarks from a variety of emerging IoT application areas, including machine learning and have a prediction error of less than 5%.

AB - Energy modelling can enable energy-aware software development and assist the developer in meeting an application's energy budget. Although many energy models for embedded processors exist, most do not account for processor-specific configurations, neither are they suitable for static energy consumption estimation. This paper introduces a set of comprehensive energy models for Arm's Cortex-M0 processor, ready to support energy-aware development of edge computing applications using either profiling- or static-analysis-based energy consumption estimation. We use a commercially representative physical platform together with a custom modified Instruction Set Simulator to obtain the physical data and system state markers used to generate the models. The models account for different processor configurations which all have a significant impact on the execution time and energy consumption of edge computing applications. Unlike existing works, which target a very limited set of applications, all developed models are generated and validated using a very wide range of benchmarks from a variety of emerging IoT application areas, including machine learning and have a prediction error of less than 5%.

KW - Arm Cortex-M0

KW - edge computing

KW - embedded systems

KW - energy modelling

KW - IoT

U2 - 10.1109/ICECS202256217.2022.9971086

DO - 10.1109/ICECS202256217.2022.9971086

M3 - Conference Contribution (Conference Proceeding)

AN - SCOPUS:85145348232

SN - 978-1-6654-8824-2

T3 - ICECS 2022 - 29th IEEE International Conference on Electronics, Circuits and Systems, Proceedings

BT - 2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 29th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2022

Y2 - 24 October 2022 through 26 October 2022

ER -

Nikov K, Georgiou K, Chamski Z, Eder K, Nunez-Yanez J. Accurate Energy Modelling on the Cortex-M0 Processor for Profiling and Static Analysis. In 2022 29th IEEE International Conference on Electronics, Circuits and Systems (ICECS). Institute of Electrical and Electronics Engineers (IEEE). 2023. (ICECS 2022 - 29th IEEE International Conference on Electronics, Circuits and Systems, Proceedings). doi: 10.1109/ICECS202256217.2022.9971086

Accurate Energy Modelling on the Cortex-M0 Processor for Profiling and Static Analysis

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

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