Stand-by power minimization through simultaneous threshold voltage selection and circuit sizing
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Dual-threshold voltage assignment with transistor sizing for low power CMOS circuits
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Energy-delay efficiency of VLSI computations
Proceedings of the 12th ACM Great Lakes symposium on VLSI
Sub-90nm technologies: challenges and opportunities for CAD
Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design
Standby power optimization via transistor sizing and dual threshold voltage assignment
Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design
Proceedings of the 2003 international symposium on Low power electronics and design
CMOS Transistor Sizing for Minimization of Energy-Delay Product
GLSVLSI '96 Proceedings of the 6th Great Lakes Symposium on VLSI
Parametric yield estimation considering leakage variability
Proceedings of the 41st annual Design Automation Conference
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Fast and exact simultaneous gate and wire sizing by Lagrangian relaxation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Traditional single-attribute optimization problems force a designer to choose either power or delay as the objective function and minimize it with constraints on other attributes. However this approach does not provide the designer with enough freedom to incorporate tradeoffs between various attributes such as leakage and delay. In this paper we present a utility theoretic approach for the joint optimization of leakage and delay. This provides a general framework for quantifying a designer's preferences for tradeoffs between leakage and delay. We show that energy-delay product (EDP) is an element of a larger class of such utility functions. The resulting multi-attribute optimization problem is modeled as a convex gate sizing problem that is solved using Geometric Programming. The resulting solution is a design point that is optimal with respect to the designer's preferences.