EdgeML
EdgeML is an open source machine learning library for enabling privacy-preserving, energy-efficient, off-the-grid intelligence in low-resource computing devices.
The Internet of Things (IoT) is poised to revolutionize our world. Billions of microcontrollers and sensors have already been deployed for predictive maintenance, connected cars, precision agriculture, personalized fitness and wearables, smart housing, cities, healthcare, and more. The dominant paradigm in these applications is that the IoT device is dumb—it just senses its environment and transmits the sensor readings to the cloud where all the intelligence resides and the decision making happens.
EdgeML enables an alternative paradigm, where even tiny resource-constrained IoT devices can run machine learning algorithms locally—without connecting to the cloud—while eliminating concerns about latency or energy and ensuring privacy and security. With EdgeML, classical machine learning tasks such as activity recognition, gesture recognition, and regression can be efficiently performed on tiny devices like the Arduino Uno, with as little as 2kB of RAM.