Sampling binary sparse coding QUBO models using a spiking neuromorphic processor-Reference-Cited by-同舟云学术

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Sampling binary sparse coding QUBO models using a spiking neuromorphic processor

Published:2023-08 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 International Conference on Neuromorphic Systems
language:
Short-container-title:

Author:

Henke Kyle¹^ORCID,Pelofske Elijah²^ORCID,Hahn Georg³^ORCID,Kenyon Garrett²^ORCID

Affiliation:

1. CCS-3 Information Sciences, Los Alamos National Lab, Los Alamos, New Mexico, United States

2. CCS-3 Information Sciences, Los Alamos National Laboratory, Los Alamos, New Mexico, United States

3. T.H. Chan School of Public Health, Harvard University, Cambridge, Massachusetts, United States

Funder

Department of Energy's (DOE) Office of Science

U.S. Department of Energy through the Los Alamos National Laboratory

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589737.3606003

Reference16 articles.

1. D-Wave. 2022. dwave-simulated-annealing. https://github.com/dwavesystems/dwave-neal. D-Wave. 2022. dwave-simulated-annealing. https://github.com/dwavesystems/dwave-neal.

2. Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook

3. Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook

4. Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems

5. Machine Learning in a Post Moore’s Law World: Quantum vs. Neuromorphic Substrates

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