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Power Estimation of CMOS Circuits by Neural Network Macromodel

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

Neural network is employed to construct the power macromodel of complementary metal-oxide-semiconductor (CMOS) integrated circuits. In contrast to previous modeling approaches, it does not require empirically constructed specialized analytical equations for the power macromodel, and obtained statistics of a circuit’s primary outputs simultaneously. It is suitable for power estimation in core-based systems-on-chips (SoCs) with pre-designed blocks. In experiments with the ISCAS-85 circuits, the average absolute relative error of the macromodel was below 5.0% for not only the average power dissipation, but also the maximum power dissipation.

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Author information

Authors and Affiliations

  1. School of Electronics Information, Wuhan University, Luoyu Road 129, Wuhan, 430079, China

    Wei Qiang, Yang Cao, Yuan-yuan Yan & Xun Gao

Authors
  1. Wei Qiang
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  2. Yang Cao
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  3. Yuan-yuan Yan
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  4. Xun Gao
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Qiang, W., Cao, Y., Yan, Yy., Gao, X. (2006). Power Estimation of CMOS Circuits by Neural Network Macromodel. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_191

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  • DOI: https://doi.org/10.1007/11760191_191

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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