Spintronic Devices for Neuromorphic Computing
2020-07
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Spintronic Devices for Neuromorphic Computing
Authors
Published Date
2020-07
Publisher
Type
Thesis or Dissertation
Abstract
Neuromorphic and spintronic computing are two different technologies that have progressed significantly in recent years. The former has seen advancement in algorithms and hardware implementations that solve previously intractable problems in data processing and classification, while the latter has seen the advent of an entirely new class of physical computing primitives. In particular, spin-torque-switched magnetic tunnel junctions and magnetelectric-ferromagnetic heterostructures are promising candidates for neuromimetic devices. Using physics-based model simulations of SHE-MTJ and ME-FM circuits, I predict the performance of various spintronic neural network formulations on standard benchmarking tasks and show that circuits like these are likely to be the neuromorphic hardware of the future.
Keywords
Description
University of Minnesota Ph.D. dissertation.July 2020. Major: Electrical Engineering. Advisor: Steven Koester. 1 computer file (PDF); ix, 142 pages.
Related to
Replaces
License
Collections
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Other identifiers
Suggested citation
Stephan, Andrew. (2020). Spintronic Devices for Neuromorphic Computing. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/241706.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.