Presentation
Combining Physics-Informed and Data-Driven Learning for Efficient Modeling of Memristive Devices
DescriptionNon-volatile memory (NVM) technologies offer ex-
citing opportunities for data-intensive computing, but the over-
whelming range of available implementations can limit the ability
to perform device-architecture co-design optimization. This work
addresses these challenges by introducing a machine learning
approach to model memristive devices, that merges physics-
informed and data-driven learning methodologies. We demon-
strate that mimicking the switching dynamics with appropriate
neural network architectures and incorporating physics modeling
equations as constraints during the training phase facilitates the
modeling task even with sparse experimental data. We validate
this training approach against traditional data-driven solutions
by comparing the respective modeling errors. Additionally, we
investigate the ability of the proposed machine learning model
to extrapolate high-level characteristics such as endurance and
switching dynamics.
citing opportunities for data-intensive computing, but the over-
whelming range of available implementations can limit the ability
to perform device-architecture co-design optimization. This work
addresses these challenges by introducing a machine learning
approach to model memristive devices, that merges physics-
informed and data-driven learning methodologies. We demon-
strate that mimicking the switching dynamics with appropriate
neural network architectures and incorporating physics modeling
equations as constraints during the training phase facilitates the
modeling task even with sparse experimental data. We validate
this training approach against traditional data-driven solutions
by comparing the respective modeling errors. Additionally, we
investigate the ability of the proposed machine learning model
to extrapolate high-level characteristics such as endurance and
switching dynamics.
Event Type
Networking
Work-in-Progress Poster
TimeSunday, June 226:00pm - 7:00pm PDT
LocationLevel 3 Lobby
DES5: Emerging Device and Interconnect Technologies
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