2026-09-18T17:32:25Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/331452022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Anant Agarwal and Eugene Weinstein.
Luong, Alda
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2006-06-19T17:45:08Z
2006-06-19T17:45:08Z
2004
2004
http://hdl.handle.net/1721.1/33145
62256184
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (leaves 58-59).
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.
by Alda Luong.
M.Eng.
59 leaves
2550303 bytes
2551718 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Bayesian inference algorithm on Raw
Thesis
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