NSF Org:	CCF Division of Computing and Communication Foundations
Recipient:	UNIVERSITY OF CALIFORNIA, LOS ANGELES
Initial Amendment Date:	September 20, 2000
Latest Amendment Date:	April 11, 2002
Award Number:	0090203
Award Instrument:	Continuing Grant
Program Manager:	Sankar Basu sabasu@nsf.gov  (703)292-7843 CCF  Division of Computing and Communication Foundations CSE  Directorate for Computer and Information Science and Engineering
Start Date:	September 1, 2000
End Date:	August 31, 2003 (Estimated)
Total Intended Award Amount:	$299,739.00
Total Awarded Amount to Date:	$299,739.00
Funds Obligated to Date:	FY 2000 = $197,802.00 FY 2002 = $101,937.00
History of Investigator:	Majid Sarrafzadeh (Principal Investigator) majid@cs.ucla.edu
Recipient Sponsored Research Office:	University of California-Los Angeles 10889 WILSHIRE BLVD STE 700 LOS ANGELES CA  US  90024-4200 (310)794-0102
Sponsor Congressional District:	36
Primary Place of Performance:	University of California-Los Angeles 10889 WILSHIRE BLVD STE 700 LOS ANGELES CA  US  90024-4200
Primary Place of Performance Congressional District:	36
Unique Entity Identifier (UEI):	RN64EPNH8JC6
Parent UEI:
NSF Program(s):	DES AUTO FOR MICRO & NANO SYS
Primary Program Source:	app-0100  app-0102  app-0199
Program Reference Code(s):	9215, HPCC
Program Element Code(s):	471000
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

Automation of a given design process requires an algorithmic analysis of it. The availability of fast and easily implementable algorithms is essential to the discipline. This proposal focuses on the physical design problems and their interaction with higher and lower-levels of the design hierarchy. Two classes of problems are being studied. First, the interaction between various cost functions in the placement problem is being investigated. In particular, relationship among net-cut, wirelength, congestion, and timing is being researched. Next, the class of algorithmic predictors and statistical predictors are being studied. One goal of this project is to show that it is possible to obtain accurate prediction very fast using floorplan and placement predictors. CAD tools associated with the above projects are under development.

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