Towards an Automated Development Environment for Parallel Computing with Reconfigurable Processing Elements

Principal Investigator:
Michael A. Langston, Department of Computer Science, University of Tennessee
Co-Principal Investigators:
Padma Raghavan, Department of Computer Science and Engineering, Penn State University
Donald W. Bouldin, Department of Electrical and Computer Engineering, University of Tennessee
Gregory D. Peterson, Department of Electrical and Computer Engineering, University of Tennessee
An adaptive computing system (ACS) offers a revolutionary combination of the performance of custom hardware and the flexibility of software by employing reconfigurable technology. A key feature of an ACS is the reconfigurable processing element which, in the current generation, is a field-programmable gate array (FPGA) chip. This research project investigates the impact of an ACS in the context of a high-performance computational grid with clusters-of-workstations, shared memory multi- processors and rapid interconnects. Suites of fast estimators are devised using approximation algorithms for FPGA mapping and partitioning. An assortment of algorithmic methods is applied. A major focus is on new heuristic and optimization strategies designed to exploit emergent mathematical techniques. Supporting software tools are also developed, with an emphasis placed on portability. Implementation testbeds are built around edge-based segmentation and related problems common to a variety of image processing applications. Recent publications and reports describe our progress.