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FPGA/MATLAB Hardware in the Loop Testbed for Stochastic Artificial Neural Networks

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Conference

ASEE 2021 Gulf-Southwest Annual Conference

Location

Waco, Texas

Publication Date

March 24, 2021

Start Date

March 24, 2021

End Date

March 26, 2021

Page Count

6

Permanent URL

https://peer.asee.org/36380

Download Count

27

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Paper Authors

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Matthew Carrano Baylor University

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Scott Koziol Baylor University

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SCOTT KOZIOL received the B.S.E.E. degree in electrical engineering from Cedarville University, Cedarville, OH, USA, in 1998, the M.S. degree in electrical engineering from Iowa State University, Ames, IA, USA, in 2000, and the M.S.M.E. degree in mechanical engineering and the Ph.D. degree in robotics from the Georgia Institute of Technology, Atlanta, GA, USA, in 2011 and 2013, respectively. He is currently an Associate Professor and Assistant Chair with the Department of Electrical and Computer Engineering, Baylor University, Waco, TX, USA

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Eugene Chabot University of Rhode Island

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Dr. Chabot is a researcher for the Department of the Navy and an adjunct faculty member at the University of Rhode Island in the department of Electrical, Computer, and Biomedical Engineering. His research focus is on navigation, autonomous systems, and applications of neuroscience with an emphasis on cognitive processing, sensory, and perception.

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Jacob Boline

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John DiCecco

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Abstract

Abstract—This paper presents a Hardware in the Loop (HWIL) testbed for evaluating a Stochastic Artificial Neural Network (SANN). The SANN is implemented in a Field Programmable Gate Array (FPGA), and this testbed allows test data to be generated on a computer using MATLAB, and sent to the FPGA SANN over a Universal Asynchronous Receiver/Transmitter (UART) interface. Initial hardware results are presented.

Carrano, M., & Koziol, S., & Chabot, E., & Boline, J., & DiCecco, J. (2021, March), FPGA/MATLAB Hardware in the Loop Testbed for Stochastic Artificial Neural Networks Paper presented at ASEE 2021 Gulf-Southwest Annual Conference, Waco, Texas. https://peer.asee.org/36380

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