BitNetMCU - Implement neural networks even on the cheapest microchips!
Embark on a captivating exploration with BitNetMCU, delving into the integration of machine learning and embedded systems. Gain invaluable insights into deploying neural networks, even on modest hardware.
Project Highlights:
Utilizes quantization aware training and fine-tuning methods, achieving over 99% accuracy on tasks like MNIST.
Operates efficiently within tight memory constraints, featuring algorithms optimized for any microcontroller.
Harnesses a PyTorch-based training pipeline for flexibility and user-friendliness.
Incorporates an Ansi-C inference engine for seamless adaptability across various microcontroller platforms.
In summary, this initiative serves as a gateway to deeper comprehension in electronics, AI, and machine learning, empowering creators to devise innovative solutions for real-world challenges in IoT, edge computing, and beyond.
More information:
The article
The GitHub
Project Highlights:
Utilizes quantization aware training and fine-tuning methods, achieving over 99% accuracy on tasks like MNIST. Operates efficiently within tight memory constraints, featuring algorithms optimized for any microcontroller. Harnesses a PyTorch-based training pipeline for flexibility and user-friendliness. Incorporates an Ansi-C inference engine for seamless adaptability across various microcontroller platforms.In summary, this initiative serves as a gateway to deeper comprehension in electronics, AI, and machine learning, empowering creators to devise innovative solutions for real-world challenges in IoT, edge computing, and beyond.
More information:
The article
The GitHub
Tim's Blogcpldcpu
I have been meaning for a while to establish a setup to implement neural network based algorithms on smaller microcontrollers. After reviewing existing solutions, I felt there is no solution that I…
