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DEEP: Developing Extremely Efficient Runtime On-Chip Power Meters

Authors:

Chen-Chia Chang,

Jiang HuAuthors Info & Claims

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

Article No.: 76, Pages 1 - 9

https://doi.org/10.1145/3508352.3549427

Published: 22 December 2022 Publication History

Abstract

Accurate and efficient on-chip power modeling is crucial to runtime power, energy, and voltage management. Such power monitoring can be achieved by designing and integrating on-chip power meters (OPMs) into the target design. In this work, we propose a new method named DEEP to automatically develop extremely efficient OPM solutions for a given design. DEEP selects OPM inputs from all individual bits in RTL signals. Such bit-level selection provides an unprecedentedly large number of input candidates and supports lower hardware cost, compared with signal-level selection in prior works. In addition, DEEP proposes a powerful two-step OPM input selection method, and it supports reporting both total power and the power of major design components. Experiments on a commercial microprocessor demonstrate that DEEP's OPM solution achieves correlation R > 0.97 in per-cycle power prediction with an unprecedented low area overhead on hardware, i.e., < 0.1% of the microprocessor layout. This reduces the OPM hardware cost by 4 -- 6× compared with the state-of-the-art solution.

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Cited By

Peng JLiang TJiang JZhang YLin ZXie ZZhang W(2024)Data-Pattern-Based Predictive On-Chip Power Meter in DNN AcceleratorIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.341297843:12(4753-4766)Online publication date: Dec-2024
https://doi.org/10.1109/TCAD.2024.3412978
Liu TZhao HLyu Y(2024)CAPEDL: Cycle-Accurate Power Estimation with Deep Learning2024 2nd International Symposium of Electronics Design Automation (ISEDA)10.1109/ISEDA62518.2024.10617476(642-647)Online publication date: 10-May-2024
https://doi.org/10.1109/ISEDA62518.2024.10617476
Zoni DGalimberti AFornaciari W(2023)A Survey on Run-time Power Monitors at the EdgeACM Computing Surveys10.1145/359304455:14s(1-33)Online publication date: 18-Apr-2023
https://dl.acm.org/doi/10.1145/3593044
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DEEP: Developing Extremely Efficient Runtime On-Chip Power Meters
1. Applied computing
  1. Arts and humanities

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Published In

cover image ACM Conferences

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

October 2022

1467 pages

ISBN:9781450392174

DOI:10.1145/3508352

Conference Chair:
Tulika Mitra
National University of Singapore
,
Program Chairs:
Evangeline Young
The Chinese University of Hong Kong
,
Jinjun Xiong
University at Buffalo (UB)

Copyright © 2022 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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Published: 22 December 2022

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ICCAD '22: IEEE/ACM International Conference on Computer-Aided Design

October 30 - November 3, 2022

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Cited By

Peng JLiang TJiang JZhang YLin ZXie ZZhang W(2024)Data-Pattern-Based Predictive On-Chip Power Meter in DNN AcceleratorIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.341297843:12(4753-4766)Online publication date: Dec-2024
https://doi.org/10.1109/TCAD.2024.3412978
Liu TZhao HLyu Y(2024)CAPEDL: Cycle-Accurate Power Estimation with Deep Learning2024 2nd International Symposium of Electronics Design Automation (ISEDA)10.1109/ISEDA62518.2024.10617476(642-647)Online publication date: 10-May-2024
https://doi.org/10.1109/ISEDA62518.2024.10617476
Zoni DGalimberti AFornaciari W(2023)A Survey on Run-time Power Monitors at the EdgeACM Computing Surveys10.1145/359304455:14s(1-33)Online publication date: 18-Apr-2023
https://dl.acm.org/doi/10.1145/3593044
Xie ZZhang TPeng Y(2023)Security and Reliability Challenges in Machine Learning for EDA: Latest Advances2023 24th International Symposium on Quality Electronic Design (ISQED)10.1109/ISQED57927.2023.10129359(1-6)Online publication date: 5-Apr-2023
https://doi.org/10.1109/ISQED57927.2023.10129359
Musthafa NJemsheer Ahmed PMuhammed Salah KMuhammed Afnan FMohammed Shibilmon OMohammed Shameel P(2023)Identification And Detection Of Tomato Plant Disease From Leaf Using Deep Reinforcement Learning2023 9th International Conference on Smart Computing and Communications (ICSCC)10.1109/ICSCC59169.2023.10335002(576-581)Online publication date: 17-Aug-2023
https://doi.org/10.1109/ICSCC59169.2023.10335002
Peng JLiang TXie ZZhang W(2023)PROPHET: Predictive On-Chip Power Meter in Hardware Accelerator for DNN2023 60th ACM/IEEE Design Automation Conference (DAC)10.1109/DAC56929.2023.10247979(1-6)Online publication date: 9-Jul-2023
https://doi.org/10.1109/DAC56929.2023.10247979

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