Title

Non-Volatile Memory and Associative Learning

Presenter Information

Chris Rowan

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

Comments

Joint project with Nicole Aldridge and Daniel Ellerbrock

This document is currently not available here.

Share

COinS
 
Apr 16th, 2:00 PM Apr 16th, 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.