Memristor-based AI Hardware for Reliable and Reconfigurable Neuromorphic Computing
Name
Choi-chanyeol-PhD-EECS-2021-thesis.pdf
Description
Thesis PDF
Size
5.61 MB
Format
Adobe PDF
Checksum (MD5)
89d6df1beb54c79db63cdde7668df3b3
Author(s)
Choi, Chanyeol
Advisor(s)
Kim, Jeehwan
Date Issued
September 2021
Publisher
Massachusetts Institute of Technology
Abstract
In the field of artificial intelligence hardware, a memristor has been proposed as an artificial synapse for creating neuromorphic computer applications. Changes in weight values in the form of conductance must be identifiable and uniform to train a neural network in memristor arrays. Because of the high mobility of metal ions in the Si switching medium, an electrochemical metallization (ECM) memory has shown a high analogue switching capacity. However, switching unpredictability is caused by the extreme stochasticity of ion transport. I demonstrated a Si memristor with alloyed conduction channels that works dependably and enables large-scale crossbar array deployment. In addition, heterogeneously integrated neuromorphic chips have been developed to allow physically reconfigurable neuromorphic computing. This thesis examines alloyed metal-based silicon memristors and stackable neuromorphic chips with heterogeneous integration for reliable and reconfigurable neuromorphic computing.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
In Copyright - Educational Use Permitted
Copyright MIT
Persistent DSpace Link
