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Tiny Machine Learning (TinyML) is an emerging field at the intersection of embedded machine learning (ML) applications, algorithms, hardware, and software. TinyML differs from mainstream machine learning (e.g., server and cloud) in that it requires not only software expertise, but also embedded-hardware expertise.
This program will emphasize hands-on experience with ML training and deployment in tiny microcontroller-based devices.The course features projects based on a TinyML program kit that includes an Arm Cortex-M4 microcontroller with onboard sensors, a camera, and a breadboard with wires—enough to unlock capabilities such as image, sound, and gesture detection. Before you know it, you’ll be implementing an entire TinyML application.
The course will also feature real-world application case studies, guided by industry leaders, that examine the challenges facing real-world TinyML deployments.
This first-of-its-kind program will be launching Fall 2020. Please submit your information if you would like to receive updates regarding the program or opportunities to take individual courses in the program as an auditor.
Vijay Janapa Reddi
Associate Professor, Harvard University
Technical Lead of TensorFlow Mobile and Embedded, Google
Lead AI Advocate, Google