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Open Hardware/Modding: Shakti Processors, 3D Printing and More

Submitted by Roy Schestowitz on Sunday 7th of July 2019 09:54:19 AM Filed under
Hardware
OSS
  • Shakti Processors: India's first open-source indigenous processor

    The Indian Institute of Technology (IIT) Madras launched a government-funded software development kit (SDK) for its open-source Shakti processor last month, in a move to help India build its first indigenous processor.
    An open-source processor is a software of which the original source code is available freely. It can be redistributed and modified.
    The open-source, since it is a part of the source code, will be a part of the software. It will be a chip installed in the CPU of the computer, similar to the tech major Intel's processor chips which are widely used in India.
    The project was funded by the Ministry of Electronics and Information Technology (MeitY). The project was set up with the aim of India building its own processors, just like China and the European Union, instead of depending on US processors.

  • VIDEO: Maker Norbert Heinz's open source granule extruder for desktop 3D printers

    Heinz who describes himself as a ‘tinkerer’ uses only open-source software and hardware for his projects. In addition to inventing things, Heinz also promotes science education, especially ‘Physical Computing’, a combination of physics and computing. On his webpage, Heinz has a substantial amount of educational material and quite a few open-source projects ready to build such as a 2D dot matrix printer and a plotter made by converting a 3D printer.

  • InAccel Releases Open-Source Logistic Regression IP Core for FPGAs

    Machine learning algorithms are extremely computationally intensive and time consuming when they must be trained on large amounts of data.

  • Make: a machine-learning toy on open-source hardware

    In the latest Adafruit video (previously) the proprietors, Limor "ladyada" Friend and Phil Torrone, explain the basics of machine learning, with particular emphasis on the difference between computing a model (hard) and implementing the model (easy and simple enough to run on relatively low-powered hardware), and then they install and run Tensorflow Light on a small, open-source handheld and teach it to distinguish between someone saying "No" and someone saying "Yes," in just a few minutes. It's an interesting demonstration of the theory that machine learning may be most useful in tiny, embedded, offline processors.

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