Non-Volatile Memory and Associative Learning
Department
Electrical and Computer Engineering
Major
Computer Engineering
Research Advisor
Wunsch, Donald C.
Advisor's Department
Electrical and Computer Engineering
Funding Source
OURE
Abstract
Neuromorphic computing is a critical tool in modern problem solving, and non-volatile memory devices like memristors mitigate massive power consumption by transistor-based implementations. Memristors retain a set conductance level even with power off, enabling many practical applications. However, most research studies use idealized simulations, ignoring hardware implementations and non-ideal traits. This project investigates the use of commercially available hardware memristors and their non-ideal properties, to analyze associative learning applications. It demonstrates that non-ideal memristor components are not only feasible for use in machine learning applications, but can actually provide beneficial results when employed in associative memory algorithms.
Biography
Chris is a senior in the Computer Engineering program at MST, and plans to graduate with his bachelor's degree in spring 2019. While attending MST, he specialized in process automation, taking courses focusing on manufacturing automation. After graduation, he will be working for MAVERICK Technologies, a PLC/HMI automation company. He has been working under his research advisor, Dr. Wunsch, and alongside his team members Daniel Ellerbrock and Nicole Aldridge, as well as with the groups counterpart at Huazhong University of Science & Technology, Yi Huang, in the OURE program for the past year.
Research Category
Engineering
Presentation Type
Oral Presentation
Document Type
Presentation
Location
Ozark Room
Presentation Date
16 Apr 2019, 2:00 pm - 2:30 pm
Non-Volatile Memory and Associative Learning
Ozark Room
Neuromorphic computing is a critical tool in modern problem solving, and non-volatile memory devices like memristors mitigate massive power consumption by transistor-based implementations. Memristors retain a set conductance level even with power off, enabling many practical applications. However, most research studies use idealized simulations, ignoring hardware implementations and non-ideal traits. This project investigates the use of commercially available hardware memristors and their non-ideal properties, to analyze associative learning applications. It demonstrates that non-ideal memristor components are not only feasible for use in machine learning applications, but can actually provide beneficial results when employed in associative memory algorithms.
Comments
Joint project with Nicole Aldridge and Daniel Ellerbrock