Abstract
Modern VLSI design methodologies and manufacturing technologies are making circuits increasingly fast. The quest for higher circuit performance and integration density stems from fields such as the telecommunication one where high speed and capability of dealing with large data sets is mandatory. The design of high-speed circuits is a challenging task, and can be carried out only if designers can exploit suitable CAD tools. Among the several aspects of high-speed circuit design, controlling power consumption is today a major issue for ensuring that circuits can operate at full speed without damages. In particular, tools for fast and accurate estimation of power consumption of highspeed circuits are required. In this paper we focus on the problem of predicting the maximum power consumption of sequential circuits. We formulate the problem as a constrained optimization problem, and solve it resorting to an evolutionary algorithm. Moreover, we empirically assess the effectiveness of our problem formulation with respect to the classical unconstrained formulation. Finally, we report experimental results assessing the effectiveness of the prototypical tool we implemented.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
7. References
S. Manne, A. Pardo, R. I. Bahar, G. D. Hachtel, F. Somenzi, E. Macii, M. Poncino, “Computing the maximum power cycles of a sequential circuit”, Proc. of IEEE/ACM DAC, 1995, pp. 23–287
C.-Y. Wang, K. Roy, “Maximum Current Estimation in CMOS Circuits Using Deterministic and Statistical Techniques”, IEEE Trans. on VLSI Systems, March 1998, pp. 134–140
M.S. Hsiao, E.M. Rudnick, J. Patel, “K2: An Estimator for Peak Sustainable Power of VLSI Circuits”, Proc. of Int. Symp. on Low Power Electronics and Design, 1997, pp. 178–183
M.S. Hsiao, E.M. Rudnick, J. Patel, “Effects of Delay Models on Peak Power Estimation of VLSI Sequential Circuits”, Proc. of IEEE/ACM ICCAD, 1997, pp. 45–51
F. Corno, M. Sonza Reorda, G. Squillero, “Optimizing Deceptive Functions with the SGClans Algorithm”, CEC’99: 1999 Congress on Evolutionary Computation, Washington DC (USA), July 1999, pp. 2190–2195
A. Ghosh, S. Devadas, K. Kuetzer, J. White, “Estimation of average switching activity in combinational and sequential circuits”, Proc. of IEEE/ACM DAC, 1992, pp. 253–259
R. E. Bryant, “Symbolic Boolean Manipulation with Ordered Binary Decision Diagrams,” ACM Computing Surveys, Vol. 24, No. 3, 1992, pp. 293–318
F. Corno, M. Rebaudengo, M. Sonza Reorda, M. Violante, “ALPS: A Peak-Power Estimation Algorithm for Sequential Circuits”, GLS-VLSI’99: 8th Great Lakes Symposium on VLSI, 1999, pp. 350–353
F. Corno, M. Sonza Reorda, G. Squillero, “The Selfish Gene Algorithm: a New Evolutionary Optimization Strategy”, SAC’98: 13th Annual ACM Symposium on Applied Computing, 1998, pp. 349–355
F. Corno, M. Sonza Reorda, G. Squillero, “A New Evolutionary Algorithm Inspired by the Selfish Gene Theory”, ICEC’98: IEEE International Conference on Evolutionary Computation, 1998, pp. 575–580
F. Corno, M. Sonza Reorda, G. Squillero, “Optimizing Deceptive Functions with the SGClans Algorithm”, CEC’99: 1999 Congress on Evolutionary Computation, 1999, pp. 2190–2195
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Corno, F., Rebaudengo, M., Sonza Reorda, M., Violante, M. (2000). Prediction of Power Requirements for High-Speed Circuits. In: Cagnoni, S. (eds) Real-World Applications of Evolutionary Computing. EvoWorkshops 2000. Lecture Notes in Computer Science, vol 1803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45561-2_24
Download citation
DOI: https://doi.org/10.1007/3-540-45561-2_24
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67353-8
Online ISBN: 978-3-540-45561-5
eBook Packages: Springer Book Archive