The Front Page for Probabilistic Spin Logic
Abstract
While probabilistic neural networks are a staple of the neural network field, their study in the context of real hardware has been limited. Probabilistic spin logic entails the study of probabilistic neurons that have real hardware counterparts. This comes under a new effort, termed Purdue-P, whose goal it is to develop efficient, probabilistic neural network hardware to solve some of today’s most difficult problems. An important step in this effort has been the development of a website, purduep.com, to act as a “front page” for the effort. This website introduces the idea of probabilistic spin logic to newcomers, houses an online web simulator and blog, and provides instructions on how to access a powerful asynchronous p-computing co-processor through the cloud. The thoughts behind the flow of content, the web simulator, and cloud access of the coprocessor constitute the crux of the thesis.
Degree
M.Sc.
Advisors
Datta, Purdue University.
Subject Area
Energy|Artificial intelligence|Computer science|Electrical engineering|Information Technology
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