Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
Estimation of average switching activity in combinational and sequential circuits
DAC '92 Proceedings of the 29th ACM/IEEE Design Automation Conference
Computing the maximum power cycles of a sequential circuit
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
K2: an estimator for peak sustainable power of VLSI circuits
ISLPED '97 Proceedings of the 1997 international symposium on Low power electronics and design
Effects of delay models on peak power estimation of VLSI sequential circuits
ICCAD '97 Proceedings of the 1997 IEEE/ACM international conference on Computer-aided design
The selfish gene algorithm: a new evolutionary optimization strategy
SAC '98 Proceedings of the 1998 ACM symposium on Applied Computing
ALPS: A Peak Power Estimation Tool for Sequential Circuits
GLS '99 Proceedings of the Ninth Great Lakes Symposium on VLSI
Maximum power estimation for CMOS circuits using deterministic and statistical approaches
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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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 high-speed 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.