{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/33145"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/33145","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Bayesian inference algorithm on Raw","abstract":"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. 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