There are over 250 billion embedded devices active in the world, and the number shipped is growing by 20% every year. They are gathering massive amounts of sensor data, far more than can ever be transmitted or processed in the cloud.
On-device machine learning gives us the ability to turn this wasted data into actionable information, and will enable a massive number of new applications over the next few years. Pete Warden digs into why embedded machine learning is so important, how to implement it on existing chips, and some of the new use cases it will unlock.
Pete Warden is the technical lead of the mobile and embedded TensorFlow Group on Google’s Brain team.
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