A reconfigurable computing framework for multi-scale cellular image processing

Authors:
Reid Porter;Jan Frigo;Al Conti;Neal Harvey;Garrett Kenyon;Maya Gokhale
Affiliations:
Space Data Systems Group, International, Space & Response Technologies Division, Los Alamos National Laboratory, Mail Stop D440, Los Alamos, NM 87545, United States;Space Data Systems Group, International, Space & Response Technologies Division, Los Alamos National Laboratory, Mail Stop D440, Los Alamos, NM 87545, United States;Space Data Systems Group, International, Space & Response Technologies Division, Los Alamos National Laboratory, Mail Stop D440, Los Alamos, NM 87545, United States;Space and Remote Sensing Sciences Group, International, Space & Response Technologies Division, Los Alamos National Laboratory, United States;Biophysics Group, Physics Division, Los Alamos National Laboratory, United States;Advanced Computing Group, Computer and Computational Sciences, Los Alamos National Laboratory, United States
Venue:
Microprocessors & Microsystems
Year:
2007

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Abstract

Cellular computing architectures represent an important class of computation that are characterized by simple processing elements, local interconnect and massive parallelism. These architectures are a good match for many image and video processing applications and can be substantially accelerated with Reconfigurable Computers. We present a flexible software/hardware framework for design, implementation and automatic synthesis of cellular image processing algorithms. The system provides an extremely flexible set of parallel, pipelined and time-multiplexed components which can be tailored through reconfigurable hardware for particular applications. The most novel aspects of our framework include a highly pipelined architecture for multi-scale cellular image processing as well as support for several different pattern recognition applications. In this paper, we will describe the system in detail and present our performance assessments. The system achieved speed-up of at least 100x for computationally expensive sub-problems and 10x for end-to-end applications compared to software implementations.