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The Rochester Checkers Player: Multi-Model Parallel Programming for Animate Vision
ROCHESTER UNIV NY DEPT OF COMPUTER SCIENCE
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Animate vision systems couple computer vision and robotics to achieve robust and accurate vision, as well as other complex behavior. These systems combine low-level sensory processing and effector output with high-level cognitive planning - all computationally intensive tasks that can benefit from parallel processing. No single model of parallel programming is likely to serve for all tasks, however. Early vision algorithms are intensely data parallel, often utilizing fine-grain parallel computations that share an image, while cognition algorithms decompose naturally by function, often consisting of loosely-coupled, coarse-grain parallel units. A typical animate vision application will likely consist of many tasks, each of which may require a different parallel programming model, and all of which must cooperate to achieve the desired behavior. These multi-model programs require an underlying software system that not only supports several different models of parallel computation simultaneously, but which also allows tasks implemented in different models to interact.
APPROVED FOR PUBLIC RELEASE