Development of machine learning models that are very tiny in footprint and take miniscule energy to make predictions. Deploy models on microcontrollers and embedded devices.
Develop models specific to your data, collected on the edge device.
Deploy models to a specific hardware or controller, such as arduino.
System to update the models over the air style, keep models updated on the edge.
Compress state of the art machine learning models to tiny footprint.
Deploy tiny models on custom hardware and off the shelf hardware.
Enable edge devices for analytical capabilities with machine learning models.
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Build deep learning models for tiny embedded edge devices to enable them to make predictions and make decisions on the edge, at a fraction of energy consumption.
Acquire & store data from the edge devices for training and testing.
Train & Compress models that can run on edge devices with very small footprint.
Deploy the devices with tensorflow lite on embedded low power devices.