Machine learning on microcontrollers enables AI

One exciting avenue in the world of AI research and development is finding ways to shrink AI algorithms to run on smaller devices closer to sensors, motors and people. Developing embedded AI applications that run machine learning on microcontrollers comes with different constraints around power, performance, connectivity and tools. Embedded AI already has various uses: identifying types of physical activity with smartphone sensors, responding to wake words in consumer electronics, monitoring industrial equipment and distinguishing family members from strangers in home security cameras. A range of new tools, such as TinyML and TensorFlow Lite, can simplify the development of smaller, more power-efficient AI algorithms. "The rise of TinyML deployed on microcontrollers enables intelligence to be distributed into more connected products in the physical world, whether they be smart home gadgets, toys, industrial sensors or otherwise," said Jason Shepherd, vice president of ecosystem at edge-computing platform Zededa.