Large-scale neuromorphic computing hardware for analog AI enabled by epitaxial random access memory

A neuromorphic computing on memristor-based crossbars is one of promising next generation analog computing methods since it features fast switching speed, extremely small cell footprint, low energy consumption for matrix-vector multiplication, capability of both storage and computing, three-dimensionality, and many analog weight steps. Although there have been intensive studies on the development of an analog memristive device and its large-scale crossbar to implement neuromorphic hardware systems for deep neural networks, only limited approaches, such as inference task, were suggested due to spatial/temporal variations and nonlinear/step-limited weight update properties. In order to address those issues, this thesis presents epitaxial random access memory and relevant techniques at material-, device-, array-, architecture-, algorithm-level. The proposed methods have great potential to improve device performance and relax the large-scale system-level requirements for analog AI computing.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Choi, Chanyeol.

Advisor dc:contributor.advisor

• Jeehwan Kim.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/124125

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/124125