Title

Non-Volatile Memory and Associative Learning

Presenter Information

Daniel Ellerbrock

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

Daniel Ellerbrock is a senior in Electrical and Computer Engineering graduating in May 2019. He has interests in non-volatile memory devices, computer architecture, and neuromorphic computing.When he graduates he will be working in the non-volatile memory systems group at Intel in California. On campus he has been involved in Kappa Alpha Order, Missouri S&T Jazz Band, and the Missouri S&T Chem E Car team. His undergraduate research is advised by Dr Donald Wunsch and is being conducted with fellow teammates Chris Rowan, Nicole Aldridge, and Yi Huang from Huazhong University of Science & Technology.

Research Category

Engineering

Presentation Type

Oral Presentation

Document Type

Presentation

Location

Ozark Room

Presentation Date

16 Apr 2019, 2:30 pm - 3:00 pm

Comments

Joint project with Nicole Aldridge 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.