Award Abstract #0540407
Collaborative Research: DDDAS-SMRP: Optimizing Signal and Image Processing in a Dynamic, Data-Driven Application System

NSF Org:	CNS Division of Computer and Network Systems
Initial Amendment Date:	September 15, 2005
Latest Amendment Date:	September 15, 2005
Award Number:	0540407
Award Instrument:	Standard Grant
Program Manager:	Anita J. LaSalle CNS Division of Computer and Network Systems CSE Directorate for Computer & Information Science & Engineering
Start Date:	December 15, 2005
Expires:	April 30, 2009 (Estimated)
Awarded Amount to Date:	$356000
Investigator(s):	Mary Hall mhall@cs.utah.edu (Principal Investigator) Pedro Diniz (Co-Principal Investigator)
Sponsor:	University of Southern California University Park Los Angeles, CA 90089 213/740-7762
NSF Program(s):	ITR-DYNAMIC DATA DRIV APP SYS
Field Application(s):
Program Reference Code(s):	HPCC, 9218
Program Element Code(s):	7581

ABSTRACT

This project will develop a dynamic, data-driven application system for signal and image processing under resource constraints. This system would lay a foundation for highly optimized implementations of fundamental signal and imaging processing computations that arise in many science and engineering problems, including image recognition, communications analysis, speech processing, querying, indexing, and retrieval from multimedia databases, and image segmentation of aerial, satellite, and astronomical images. The proposed multidisciplinary approach optimizes from algorithm specification, to mathematical representation, to software and hardware (FPGA) implementation, based on properties of data and unique requirements of

the environment and the target hardware device.

The novelty of the system is twofold: (1) it performs joint optimization

across mathematical, software and hardware (system-on-a-chip FPGA) domains; and, (2) it is a dynamic, data-driven system in that signal-processing transforms are tailored to algorithm requirements and input signals, for reduced distortion and increased compression, and the system can be queried and steered during execution. Implementations are based on the best mathematical formulation of the problem coupled with automated selection of the best implementation among a space of alternatives, through the integration of models relating mathematical properties to implementation

behavior. Both hardware and software optimization are treated in a unified way. It is anticipated that with these methods the design-time will be decreased by two orders of magnitude or more, compared to implementations derived in a traditional way. Because the proposed system can explore a broad range of implementations that exceed the capabilities of a human designer, the implementations derived by the approach pursued in the project may even exhibit lower resource costs and higher performance. This research will provide a foundation for signal processing at all scales, providing key building blocks for engineers to build complex, distributed networks of adaptive signal processing sensors.

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