Deep Learning at Mote Scale

Опубликовано 1 августа 2018, 16:53

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, etc. 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.

We envision an alternative paradigm where even tiny, resource-constrained IoT devices can run machine learning algorithms locally without necessarily connecting to the cloud. This enables a number of critical scenarios, beyond the pale of the traditional paradigm, where it is not desirable to send data to the cloud due to concerns about latency, connectivity, energy, privacy and security.

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