Journal
SUPERCONDUCTOR SCIENCE & TECHNOLOGY
Volume 34, Issue 1, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/1361-6668/abc569
Keywords
artificial neural networks; backpropagation; hardware ANN learning; Josephson junction; superconducting quantum interferometer
Categories
Funding
- Russian Science Foundation [18-72-10118]
- Council of the President of the Russian Federation for State Support of Young Scientists and Leading Scientific Schools [MD-186.2020.8]
- RFBR [19-37-90020]
- Foundation for the Development of Theoretical Physics and Mathematics 'BASIS'
Ask authors/readers for more resources
This study introduces an energy-efficient adiabatic learning neuro cell for on-chip learning of adiabatic superconducting artificial neural networks. The static and dynamic characteristics of the learning cell have been researched, and optimization of its parameters was conducted through simulations of a multi-layer neural network with the resilient propagation method.
An energy-efficient adiabatic learning neuro cell is proposed. The cell can be used for on-chip learning of adiabatic superconducting artificial neural networks. The static and dynamic characteristics of the proposed learning cell have been investigated. Optimization of the learning cell parameters was performed within simulations of the multi-layer neural network supervised learning with the resilient propagation method.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available
Share Paper
