A Run-Time Re-configurable Parametric Architecture for Local Neighborhood Image Processing

Abstract

We propose a run-time re-configurable parametric architecture (fabric) for local neighborhood image processing. The proposed architecture is composed of polymorphous cells where each cell accesses neighborhood data from a local cell memory, and executes a neighborhood function sequentially. The architecture is flexible since different neighborhood functions can be implemented by rewriting a cell's software micro-code. High throughput is achieved because many cells execute concurrently. We show that for a satellite image feature extraction application, our architecture, implemented on Stratix II and Virtex 2 field programmable gate arrays, achieves similar performance, hardware resource utilization, and throughput as a fully pipelined systolic array architecture, yet offers imp roved flexibility to the developer. We compare and contrast these two architectures for their usability to the image processing community