The Distributed Macro Controller for GSIMD Machines

Holsztynski, W.; Raghavan, R.

doi:10.1007/978-1-4684-5511-3_35

Cited by 2 publications

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“…A complete architecture should also control its activities (the algorithms it applies) in real time, in response to external stimuli or changes, or in response to its own internal judgments as they arise. We describe briefly our approaches to the above italicized goals except for the issue of real-time control which has been dealt with elsewhere (see for example [HR87], [JNR9OJ).…”

Section: System Overviewmentioning

confidence: 99%

Image recognition and learning in parallel networks

Raghavan¹,

Adams²,

Nguyen³

et al. 1990

SPIE Proceedings

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We describe the design of an image-recognition system and its performance on multi-sensor imagery.The system satisfies a list of natural requirements, which includes locality of inferences (for efficient VLSI implementation), incorporation of prior knowledge, multi-level hierarchies, and iterative improvement. Two of the most important new features are: a uniform parallel architecture for low-, mid-and high-level vision; and achievement of recognition through short-, as opposed to its long-time behavior, of a dynamical system. Robustness depends on collective effects rather than high precision of the processing elements. Th resulting network displays a balance of high speed and small size. We also indicate how this architecture is related to the Dempster-Shafer calculus for combining evidence from multiple sources, and present novel methods of learning in such networks, including one that addresses the integration of model-based and data-driven approaches.

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Section: System Overviewmentioning

confidence: 99%

Image recognition and learning in parallel networks

Raghavan¹,

Adams²,

Nguyen³

et al. 1990

SPIE Proceedings

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“…Our approach has been [2] to design a controller which is a natural evolution of the Von-Neumann machine: one in which a hierarchical system of such machines is introduced (Figure 1). Higher levels of the hierarchy represent "strategists" relative to the lower levels which are the "tacticians".…”

Section: An Approach For Real-time Controlmentioning

confidence: 99%

Fine grain parallel processors and real-time applications: MIMD controller/SIMD array

Raghavan¹,

Jung²,

Nguyen³

[1990] Proceedings. 10th International Conference on Pattern Recognition

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AB ST R ACTWe discuss important issues in the applications of massively parallel architectures for real-time cognition. One of these is real-time control of the array processor. Systems accommodating massive data parallelism are faced with the critical issue of how to incorporate adaptive programming decisions 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 MlMD controller that we have constructed for a SlMD array.We then discuss the efficiency of a SlMD architecture as a computational model with reference to several functions important in vision applications and that have been benchmarked on our system. Applications that we have implemented involving real-time control are also described.

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The Distributed Macro Controller for GSIMD Machines

Cited by 2 publications

References 0 publications

Image recognition and learning in parallel networks

Image recognition and learning in parallel networks

Fine grain parallel processors and real-time applications: MIMD controller/SIMD array

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