/brains-py

A python package to support the study of Dopant Network Processing Units as hardware accelerators for non-linear operations. Its aim is to support key functions for hardware setups and algorithms related to searching functionality on DNPUs and DNPU architectures both in simulations and in hardware.

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

brains-py CircleCI Tools Theory

A python package based on PyTorch and NumPy to support the study of Dopant Network Processing Units (DNPUs) [1][2] as low-power computational units in the context of neural networks. The aim of the package is to support key functions for hardware setups and algorithms related to searching functionality on DNPUs and DNPU architectures both in simulations and in hardware. The package is part of the brains-py project, a set of python libraries to support the development of nano-scale in-materio hardware for designing hardware accelerators for neural-network like operations. This package is based on several peer-reviewed scientific publications.

1. Instructions

You can find detailed instructions for the following topics on the wiki:

2. License and libraries

This code is released under the GNU GENERAL PUBLIC LICENSE Version 3. Click here to see the full license. The package relies on the following libraries:

  • General support libraries:
    • PyTorch, NumPy, tensorboard, tqdm and matplotlib
  • Drivers and hardware setups:
    • nidaqmx, pypiwin32
  • Configurations:
    • pyyaml
  • Linting, and testing:
    • unittest, mypy, flake8, coverage

3. Related scientific publications

[1] Chen, T., van Gelder, J., van de Ven, B., Amitonov, S. V., de Wilde, B., Euler, H. C. R., ... & van der Wiel, W. G. (2020). Classification with a disordered dopant-atom network in silicon. Nature, 577(7790), 341-345. https://doi.org/10.1038/s41586-019-1901-0

[2] HCR Euler, U Alegre-Ibarra, B van de Ven, H Broersma, PA Bobbert and WG van der Wiel (2020). Dopant Network Processing Units: Towards Efficient Neural-network Emulators with High-capacity Nanoelectronic Nodes. https://arxiv.org/abs/2007.12371](https://arxiv.org/abs/2007.12371)

[3] HCR Euler, MN Boon, JT Wildeboer, B van de Ven, T Chen, H Broersma, PA Bobbert, WG van der Wiel (2020). A Deep-Learning Approach to Realising Functionality in Nanoelectronic Devices. https://doi.org/10.1038/s41565-020-00779-y

4. Acknowledgements

This package has been created and it is maintained by the Brains team of the NanoElectronics research group at the University of Twente. It has been designed by:

  • Dr. Unai Alegre-Ibarra, @ualegre (u.alegre@utwente.nl): Project lead, including requirements, design, implementation, maintenance, linting tools, testing and documentation (Jupyter notebooks, Wiki and supervision of file by file documentation).
  • Dr. Hans Christian Ruiz-Euler, @hcruiz (h.ruiz@utwente.nl): Initial design and implementation of major features both in this repository and in the legacy SkyNEt repository and in this one.

With the contribution of:

Other minor contributions might have been added, in form of previous scripts that have been improved and restructured from SkyNEt, and the authorship remains of those people who collaborated in it.

This project has received financial support from:

  • University of Twente
  • Dutch Research Council
    • HTSM grant no. 16237
    • Natuurkunde Projectruimte grant no. 680-91-114
  • Horizon Europe research and innovation programme
    • Grant no. 101046878
  • Toyota Motor Europe N.V.
  • Deutsche Forschungsgemeinschaft
    • Project 433682494 – SFB 1459