Title

Non-Volatile Memory and Associative Learning

Presenter Information

Nicole Aldridge

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

Comments

Joint project with Daniel Ellerbrock and Chris Rowan

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Apr 16th, 2:30 PM Apr 16th, 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.