A heuristic for optimizing stochastic activity networks with applications to statistical digital circuit sizing

A heuristic for optimizing stochastic activity networks with applications to statistical digital circuit sizing (2004)

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by Seung-jean Kim , Stephen P. Boyd , Sunghee Yun , Dinesh D. Patil , Mark A. Horowitz

Venue:	IEEE Transactions on Circuits and Systems-I
Citations:	8 - 4 self

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Datum	Value	Source
TITLE	A heuristic for optimizing stochastic activity networks with applications to statistical digital circuit sizing	INFERENCE
AUTHOR NAME	Seung-jean Kim	SVM HeaderParse 0.2
AUTHOR AFFIL	1 Information Systems Laboratory, Department of Electrical Engineering, Stanford University,	SVM HeaderParse 0.2
AUTHOR ADDR	Stanford, CA 94305.	SVM HeaderParse 0.2
AUTHOR NAME	Stephen P. Boyd	SVM HeaderParse 0.2
AUTHOR AFFIL	1 Information Systems Laboratory, Department of Electrical Engineering, Stanford University,	SVM HeaderParse 0.2
AUTHOR ADDR	Stanford, CA 94305.	SVM HeaderParse 0.2
AUTHOR NAME	Sunghee Yun	SVM HeaderParse 0.2
AUTHOR AFFIL	1 Information Systems Laboratory, Department of Electrical Engineering, Stanford University,	SVM HeaderParse 0.2
AUTHOR ADDR	Stanford, CA 94305.	SVM HeaderParse 0.2
AUTHOR NAME	Dinesh D. Patil	SVM HeaderParse 0.2
AUTHOR AFFIL	2 Computer Systems Laboratory, Department of Electrical Engineering, Stanford University,	SVM HeaderParse 0.2
AUTHOR ADDR	Stanford, CA 94305.	SVM HeaderParse 0.2
AUTHOR NAME	Mark A. Horowitz	SVM HeaderParse 0.2
AUTHOR AFFIL	2 Computer Systems Laboratory, Department of Electrical Engineering, Stanford University,	SVM HeaderParse 0.2
AUTHOR ADDR	Stanford, CA 94305.	SVM HeaderParse 0.2
ABSTRACT	A deterministic activity network (DAN) is a collection of activities, each with some duration, along with a set of precedence constraints, which specify that activities begin only when certain others have finished. One critical performance measure for an activity network is its makespan, which is the minimum time required to complete all activities. In a stochastic activity network (SAN), the durations of the activities and the makespan are random variables. The analysis of SANs is quite involved, but can be carried out numerically by Monte Carlo analysis. This paper concerns the optimization of a SAN, i.e., the choice of some design variables that affect the probability distributions of the activity durations. We concentrate on the problem of minimizing a quantile (e.g., 95%) of the makespan, subject to constraints on the variables. This problem has many applications, ranging from project management to digital integrated circuit (IC) sizing (the latter being our motivation). While there are effective methods for optimizing DANs, the SAN optimization problem is much more difficult; the few existing methods cannot handle large-scale problems.	SVM HeaderParse 0.2
YEAR	2004	INFERENCE
VENUE	IEEE Transactions on Circuits and Systems-I	INFERENCE
VENUE TYPE	TECHREPORT	INFERENCE
PAGES	397--430	INFERENCE
VOLUME	8	INFERENCE
TECH	Technical report	INFERENCE
CITATIONS	96 found	ParsCit 1.0