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Dissertation/Thesis Abstract

A General Purpose Neural Processor
by Mountain, David Jerome, Ph.D., University of Maryland, Baltimore County, 2017, 171; 10268058
Abstract (Summary)

Computer applications are evolving from traditional scientific and numerical calculations, to a more diverse set of uses including speech recognition, robotics, and analytics. This has created a fertile environment for the investigation of non-traditional programming approaches and models of computing, inspired by neuroscience, often termed neuromorphic computing. Neural nets have emerged as one of the primary neuromorphic computing approaches; von Neumann architectures, conceived for scientific computing applications are not optimized for neural nets.

This research focuses on developing a general purpose computer architecture optimized for neural net based applications. The architecture is useful for a variety of learning algorithms, and is evaluated across a spectrum of potential applications. Both traditional and emerging technologies are explored, with trade-offs being made based on the most important system level metrics.

Indexing (document details)
Advisor: Joshi, Anupam, Taha, Tarek
Commitee: Choa, Fow-Sen, Marinella, Matthew, Pinkston, John, Zhu, Ting
School: University of Maryland, Baltimore County
Department: Computer Engineering
School Location: United States -- Maryland
Source: DAI-B 78/10(E), Dissertation Abstracts International
Subjects: Computer Engineering
Keywords: Computer architecture, Cybersecurity, Memristors, Neural nets, Neuromorphic computing
Publication Number: 10268058
ISBN: 978-1-369-81047-9
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