Bayesian inference algorithm on Raw

• Main Author: Luong, Alda
• Other Authors: Anant Agarwal and Eugene Weinstein.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2006
• Subjects: Electrical Engineering and Computer Science.
• Online Access: http://hdl.handle.net/1721.1/33145

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.