brains-py, A framework to support research on energy-efficient unconventional hardware for machine learning

Python Submitted 04 December 2022Published 08 October 2023
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Editor: @arfon (all papers)
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Authors

Unai Alegre-Ibarra (0000-0001-5957-7945), Hans-Christian Ruiz Euler, Humaid A.Mollah, Bozhidar P. Petrov, Srikumar S. Sastry, Marcus N. Boon, Michel P. de Jong, Mohamadreza Zolfagharinejad, Florentina M. j. Uitzetter, Bram van de Ven, António J. Sousa de Almeida, Sachin Kinge, Wilfred G. van der Wiel

Citation

Alegre-Ibarra et al., (2023). brains-py, A framework to support research on energy-efficient unconventional hardware for machine learning. Journal of Open Source Software, 8(90), 5573, https://doi.org/10.21105/joss.05573

@article{Alegre-Ibarra2023, doi = {10.21105/joss.05573}, url = {https://doi.org/10.21105/joss.05573}, year = {2023}, publisher = {The Open Journal}, volume = {8}, number = {90}, pages = {5573}, author = {Unai Alegre-Ibarra and Hans-Christian Ruiz Euler and Humaid A.Mollah and Bozhidar P. Petrov and Srikumar S. Sastry and Marcus N. Boon and Michel P. de Jong and Mohamadreza Zolfagharinejad and Florentina M. j. Uitzetter and Bram van de Ven and António J. Sousa de Almeida and Sachin Kinge and Wilfred G. van der Wiel}, title = {brains-py, A framework to support research on energy-efficient unconventional hardware for machine learning}, journal = {Journal of Open Source Software} }
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Dopant network processing units (DNPUs) Material Learning Machine Learning Hardware design Efficient Computing Materials Science

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ISSN 2475-9066