Superconducting Neural Networks for Faster Machine Learning
ORAL
Abstract
Next-generation neural networks have the potential to deliver advanced performance and higher processing speeds for applications in machine learning and artificial intelligence. MIT Lincoln Laboratory is investigating the feasibility of a superconducting neural network to support faster and lower-energy computing. We will present a conceptual framework for a superconducting neural network and its related benchmarks, along with simulation results for simple circuits.
DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.
This material is based upon work supported by the Under Secretary of Defense for Research and Engineering under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Under Secretary of Defense for Research and Engineering.
DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.
This material is based upon work supported by the Under Secretary of Defense for Research and Engineering under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Under Secretary of Defense for Research and Engineering.
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Presenters
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Alexandra Day
MIT Lincoln Laboratory, Massachusetts Institute of Technology MIT
Authors
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Alexandra Day
MIT Lincoln Laboratory, Massachusetts Institute of Technology MIT
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Alexander Wynn
Massachusetts Institute of Technology MIT
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Evan Golden
MIT Lincoln Laboratory, Massachusetts Institute of Technology MIT