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.
|Advisor:||Joshi, Anupam, Taha, Tarek|
|Commitee:||Choa, Fow-Sen, Marinella, Matthew, Pinkston, John, Zhu, Ting|
|School:||University of Maryland, Baltimore County|
|School Location:||United States -- Maryland|
|Source:||DAI-B 78/10(E), Dissertation Abstracts International|
|Keywords:||Computer architecture, Cybersecurity, Memristors, Neural nets, Neuromorphic computing|
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