brains-py tasks

A python package based on the brains-py library to peform benchmark tests and tasks for studying the capacity of the boron-doped silicon devices. The package is part of the brains-py project, a set of python libraries to support the development of nano-scale in-materio hardware neural-network accelerators.

• : 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.
• : A python package for creating surrogate models of nano-electronic materials.

1. General description

This package supports the following tasks:

• Boolean classifier:
  • A single boolean gate classifier
  • VC-Dimension tests
  • Capacity tests for checking the performance on multiple VC-Dimensions
• Ring classifier:
  • A particular ring classifier with a given separation gap
  • Multiple runs on a ring classifier with a given separation gap
  • Capacity tests for checking the performance on multiple separation gaps for several runs

2. Installation instructions

The installation instructions differ depending on whether if you want to install as a developer or as a user of the library. Please follow the instructions that are most suitable for you:

• User instructions
• Developer instructions

3. 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:

• brainspy
• matplotlib

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 and developed by: 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 ([email protected]): 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 ([email protected]): 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:

• Marcus Boon: @Mark-Boon: The on-chip gradient descent. The initial structure for the CDAQ to NiDAQ drivers in the legacy SkyNEt repository.
• Dr. ir. Michel P. de Jong @xX-Michel-Xx ([email protected]): Testing and identification of bugs, especially on the installation procedure.
• Florentina Min Joo Uitzetter: The genetic algorithm as shown in the legacy SkyNEt repository.
• Antonio J. Sousa de Almeida @ajsousal ([email protected]): Checking and upgrading drivers and National Instruments equipment from the labs.
• Bram van de Ven, @bbroo1 ([email protected]) : General improvements and testing of the different hardware drivers and devices and documentation.
• Mohamadreza Zolfagharinejad @mamrez ([email protected]): Writing of some of the examples in Jupyter notebooks (IV curves and surrogate model generation).

Some of the code present in this project has been refactored from the skynet legacy project. The original contributions to the scripts, which are the base of this project, can be found at skynet, and the authorship remains of those people who collaborated in it. Using existing scripts from skynet, a whole new structure has been designed and developed to be used as a general purpose python library.