Edge ML: tinyML

tinyML Summit | Home

Edge Machine Learning with Zach Shelby - Software Engineering Daily

Devices on the edge are becoming more useful with improvements in the machine learning ecosystem. TensorFlow Lite allows machine learning models to run on microcontrollers and other devices with only kilobytes of memory. Microcontrollers are very low-cost, tiny computational devices. They are cheap, and they are everywhere.

The low-energy embedded systems community and the machine learning community have come together with a collaborative effort called tinyML. tinyML represents the improvements of microcontrollers, lighter weight frameworks, better deployment mechanisms, and greater power efficiency.

TinyML is giving hardware new life | TechCrunch

Why TinyML is a giant opportunity | VentureBeat

250 billion microcontrollers in our printers, TVs, cars, and pacemakers can now perform tasks that previously only our computers and smartphones could handle. All of our devices and appliances are getting smarter thanks to microcontrollers.

TinyML represents a collaborative effort between the embedded ultra-low power systems and machine learning communities, which traditionally have operated largely independently. This union has opened the floodgates for new and exciting applications of on-device machine learning