Non-Volatile Memory and Associative Learning
Department
Electrical and Computer Engineering
Major
Computer Engineering & Computer Science
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
Nicole Aldridge is a senior student studying both Computer Engineering and Computer Science. She is originally from Bloomington, Illinois, and will be graduating in May 2019 before starting full time at Intel Corporation. Her undergraduate research is coordinated with fellow students Daniel Ellerbrock and Chris Rowan, as well as Dr. Donald Wunsch in the ECE department at S&T and Yi Huang at Huazhong University of Science and Technology. On campus, she is involved in the Varsity Track and Field Team, Chi Omega Fraternity, and holds positions as Vice President of Internal Affairs in Society of Women Engineers, and Head Ambassador within the Admissions office.
Research Category
Engineering
Presentation Type
Oral Presentation
Document Type
Presentation
Award
Engineering oral presentation, First place
Location
Ozark Room
Presentation Date
16 Apr 2019, 2:30 pm - 3:00 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 Daniel Ellerbrock and Chris Rowan