A Methodology for Guided Behavioral-Level Optimization

A Methodology for Guided Behavioral-Level Optimization (1998)

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by Lisa Guerra , Miodrag Potkonjak , Jan Rabaey

Venue:	PROC. 35TH ACM DESIGN AUTOMATION CONF. (DAC
Citations:	6 - 0 self

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Datum	Value	Source
TITLE	A Methodology for Guided Behavioral-Level Optimization	user correction - Legacy Corrections
AUTHOR NAME	Lisa Guerra	user correction
AUTHOR AFFIL	Advanced VLSI Architecture Group Computer Science Department EECS Department; Rockwell Semiconductor Systems University of California University of California	user correction
AUTHOR ADDR	Newport Beach, CA Los Angeles, CA Berkeley, CA	user correction
AUTHOR NAME	Miodrag Potkonjak	user correction
AUTHOR AFFIL	Advanced VLSI Architecture Group Computer Science Department EECS Department; Rockwell Semiconductor Systems University of California University of California	user correction
AUTHOR ADDR	Newport Beach, CA Los Angeles, CA Berkeley, CA	user correction
AUTHOR NAME	Jan Rabaey	user correction
AUTHOR AFFIL	Advanced VLSI Architecture Group Computer Science Department EECS Department; Rockwell Semiconductor Systems University of California University of California	user correction
AUTHOR ADDR	Newport Beach, CA Los Angeles, CA Berkeley, CA	user correction
ABSTRACT	Optimization at the early stages of design are crucial. However, due to an overwhelming number of design and optimization options, design exploration is often conducted in a qualitative, ad-hoc manner. This paper presents a methodology and interactive environment for guiding the exploration process. A prototype targeting behavioral-level optimization for datapath-intensive ASIC implementations has been developed. The key to the approach is encapsulated knowledge about the various optimizations and a set of techniques to automatically extract the "essence" of a design description. At each stage in the exploration process, the system suggests and ranks potential optimizations, both in terms of immediate and longer-term impact. It also provides evaluations of the design and of the likely affects each optimization will have on metrics like power and performance. In the new approach, the designer is responsible for making the actual optimization selections. However, using the provided guidance, designers can make decisions in a more informed manner, and therefore can explore the design solution space more effectively. The effectiveness of the approach is demonstrated on a number of designs.	user correction - Legacy Corrections
YEAR	1998	INFERENCE
VENUE	PROC. 35TH ACM DESIGN AUTOMATION CONF. (DAC	user correction
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	309--314	INFERENCE
CITATIONS	22 found	ParsCit 1.0