Machine Learning-Based Microarchitecture- Level Power Modeling of CPUs-Reference-Cited by-同舟云学术

Machine Learning-Based Microarchitecture- Level Power Modeling of CPUs

Published:2023-04-01 Issue:4 Volume:72 Page:941-956
ISSN:0018-9340
Container-title:IEEE Transactions on Computers
language:
Short-container-title:IEEE Trans. Comput.

Author:

Kumar Ajay Krishna Ananda¹^ORCID,Al-Salamin Sami²^ORCID,Amrouch Hussam³^ORCID,Gerstlauer Andreas¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA

2. Hyperstone®, Konstanz, Germany

3. Chair of Semiconductor Test and Reliability (STAR) in the Computer Science, Electrical Engineering Faculty, University of Stuttgart, Stuttgart, Germany

Funder

Samsung GRO Program

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Hardware and Architecture,Theoretical Computer Science,Software

Link

https://ieeexplore.ieee.org/ielam/12/10065566/9810811-aam.pdf

Reference45 articles.

1. New methodology for early-stage, microarchitecture-level power-performance analysis of microprocessors

2. Learning-based power modeling of system-level black-box IPs

3. A Multi-Granularity Power Modeling Methodology for Embedded Processors

4. Dynamic Power and Performance Back-Annotation for Fast and Accurate Functional Hardware Simulation

5. Learning-based CPU power modeling;ananda kumar;Proc ACM/IEEE Workshop Mach Learn CAD,2019

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