Bayesian inference algorithm on Raw
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Anant Agarwal and Eugene Weinstein.
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This work explores the performance of Raw, a parallel hardware platform developed at MIT, running a Bayesian inference algorithm. Motivation for examining this parallel system is a growing interest in creating a self-learning and cognitive processor, which these hardware and software components can potentially produce. The Bayesian inference algorithm is mapped onto Raw in a variety of ways to try to account for the fact that different implementations give different processor performance. Results for the processor performance, determined by looking at a wide variety of metrics look promising, suggesting that Raw has the potential to successfully run such algorithms.
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (leaves 58-59).
DepartmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.