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Proceedings Paper

Massively parallel processors in real-time applications
Author(s): Ken K. Jung; H. T. Nguyen; Raghu Raghavan; Hoa D. Truong
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Paper Abstract

In previous work, we have discussed the issue of real-time control, which is important for the effective application of massively parallel architectures in real-time, or near real-time, signal processing. We first briefly review our work on how to incorporate adaptive programming decisions in systems accommodating massive data parallelism without losing machine efficiency. This involves re-examination of functions to be performed by the software versus those performed by the hardware. Our discussion is based on a Multiple Instruction Multiple Data (MIMD) controller that we have constructed for a Geometric Single Instruction Single Data (GSIMD) array. We then address two more issues facing a GSIMD architecture for its efficient use, especially in vision applications. These are: how to treat the overlap in small neighborhood operations when a large image array is segmented for processing on a smaller GSIMD array, and how to transmit small amounts of information between the array processor and external machines for adaptive signal processing. We describe the architecture of our hardware solutions.

Paper Details

Date Published: 1 July 1990
PDF: 13 pages
Proc. SPIE 1246, Parallel Architectures for Image Processing, (1 July 1990); doi: 10.1117/12.19573
Show Author Affiliations
Ken K. Jung, Lockheed Palo Alto Research Lab. (United States)
H. T. Nguyen, Lockheed Palo Alto Research Lab. (United States)
Raghu Raghavan, Lockheed Palo Alto Research Lab. (United States)
Hoa D. Truong, Lockheed Palo Alto Research Lab. (United States)


Published in SPIE Proceedings Vol. 1246:
Parallel Architectures for Image Processing
Joydeep Ghosh; Colin G. Harrison, Editor(s)

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