research-article

Energy-demand estimation of embedded devices using deep artificial neural networks

Authors:

Benedict Herzog,

Wolfgang Schröder-PreikschatAuthors Info & Claims

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

Pages 617 - 624

https://doi.org/10.1145/3297280.3297338

Published: 08 April 2019 Publication History

Abstract

The need for high performance in embedded devices grows at a breathtaking pace. Embedded processors that satisfy the hunger for superlative processing power share a common issue: the increasing performance leads to growing energy demands during operation. As energy remains a limited resource to embedded devices, it is critical to optimise software components for low power. Low-power software needs energy models which, however, are increasingly difficult to create as to the complexity of today's devices.

In this paper we present a black-box approach to construct precise energy models for complex hardware devices. We apply machine-learning techniques in combination with fully automatic energy measurements and evaluate our approach with an ARM Cortex platform. We show that our system estimates the energy demand of program code with a mean percentage error of 1.8% compared to the results of energy measurements.

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Cited By

Köhler SHerzog BHerzog HVögele MWenzel LPolze AHönig T(2024)Carbon-Aware Memory PlacementACM SIGEnergy Energy Informatics Review10.1145/3698365.36983724:3(39-45)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3698365.3698372
Müller KWeidner JFranchi NWägemann P(2024)TinyEP: TinyML-Enhanced Energy Profiling for Extreme Edge DevicesIEEE Access10.1109/ACCESS.2024.352008912(193747-193762)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3520089
Köhler SHerzog BHofmeier HVögele MWenzel LPolze AHönig TChien AEilam TPorter GAnderson TJosephson CPark J(2023)Carbon-Aware Memory PlacementProceedings of the 2nd Workshop on Sustainable Computer Systems10.1145/3604930.3605714(1-7)Online publication date: 9-Jul-2023
https://dl.acm.org/doi/10.1145/3604930.3605714
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Energy-demand estimation of embedded devices using deep artificial neural networks

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cover image ACM Conferences

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

April 2019

2682 pages

ISBN:9781450359337

DOI:10.1145/3297280

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University, Marietta, Georgia
,
George A. Papadopoulos
University of Cyprus, Nicosia, Cyprus

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGAPP: ACM Special Interest Group on Applied Computing

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New York, NY, United States

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Published: 08 April 2019

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SAC '19

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SIGAPP

SAC '19: The 34th ACM/SIGAPP Symposium on Applied Computing

April 8 - 12, 2019

Limassol, Cyprus

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SAC '25

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The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

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Cited By

Köhler SHerzog BHerzog HVögele MWenzel LPolze AHönig T(2024)Carbon-Aware Memory PlacementACM SIGEnergy Energy Informatics Review10.1145/3698365.36983724:3(39-45)Online publication date: 30-Sep-2024
https://dl.acm.org/doi/10.1145/3698365.3698372
Müller KWeidner JFranchi NWägemann P(2024)TinyEP: TinyML-Enhanced Energy Profiling for Extreme Edge DevicesIEEE Access10.1109/ACCESS.2024.352008912(193747-193762)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3520089
Köhler SHerzog BHofmeier HVögele MWenzel LPolze AHönig TChien AEilam TPorter GAnderson TJosephson CPark J(2023)Carbon-Aware Memory PlacementProceedings of the 2nd Workshop on Sustainable Computer Systems10.1145/3604930.3605714(1-7)Online publication date: 9-Jul-2023
https://dl.acm.org/doi/10.1145/3604930.3605714
Shim JHan BKim YKim J(2022)DeepPM: Transformer-based Power and Performance Prediction for Energy-Aware Software2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE54114.2022.9774589(1491-1496)Online publication date: 14-Mar-2022
https://doi.org/10.23919/DATE54114.2022.9774589
Herzog BReif SHemp JHönig TSchröder-Preikschat W(2022)Resource-demand Estimation for Edge Tensor Processing UnitsACM Transactions on Embedded Computing Systems10.1145/352013221:5(1-24)Online publication date: 8-Oct-2022
https://dl.acm.org/doi/10.1145/3520132
Fricke FScharoba SRachuj SKonopik AKluge FHofstetter GReichenbach M(2022)Application Runtime Estimation for AURIX Embedded MCU Using Deep LearningEmbedded Computer Systems: Architectures, Modeling, and Simulation10.1007/978-3-031-15074-6_15(235-249)Online publication date: 3-Jul-2022
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Herzog BReif SPreis JSchröder-Preikschat WHönig T(2021)The Price of Meltdown and SpectreProceedings of the 14th European Workshop on Systems Security10.1145/3447852.3458721(8-14)Online publication date: 26-Apr-2021
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Wu CCui YJi CKuo TXue C(2020)Pruning Deep Reinforcement Learning for Dual User Experience and Storage Lifetime Improvement on Mobile DevicesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2020.301280439:11(3993-4005)Online publication date: Nov-2020
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Herzog BHönig TSchröder-Preikschat WPlauth MKöhler SPolze A(2019)Bridging the GapProceedings of the Tenth ACM International Conference on Future Energy Systems10.1145/3307772.3330176(428-430)Online publication date: 15-Jun-2019
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