tinyML EMEA 2022 Danilo Pau: A framework of algorithms and associated tool for on-device tiny…
A framework of algorithms and associated tool for on-device tiny learning
Danilo PAU, Technical Director, IEEE and ST Fellow, STMicroelectronics
Imagine one needs to detect anomalies in an electro cardio diagram in a robust manner considering working conditions variabilities in terms of carry position and person under monitoring. Consider using heterogeneous sensors needed to monitor a water distribution system to check its reliability. Suppose the need to detect oil leaks using a remote imaging system placed inside a 195 meters toll wind turbine in the Walney Extension located in the Irish Sea. Or consider the urgent need to monitor adversarial environments influenced by sudden climatic changes that can put at risk human lives. Finally consider applying adaptivity to field motor control.
At a first sight these cases seem not to have anything in common, however at a deeper look they have the same needs to address since examples of cyber-physical systems. They shall manage variable data distributions, learn from them since they can be hardly modelized in laboratory, deploy distributed artificial intelligence closer to the physical world either for sensing and for actuation, automatizing anomaly detections and more importantly generate automatically high-level notifications through the cyber world with intermediate processing inside the object world.
And then anyone starts to understand how important it is for the tiny machine learning community to develop intelligent technologies based on the capability to learn from constantly changing environments and on the field by using tiny, embedded devices to realize systems that can be personalized, easily and massively deployable throughout the world.
Today anyone is used to owning sensors. Any IoT or mobile device anyone owns has got sensors (and may be actuators) inside which we buy. However, this approach already limits the market opportunities, innovation, and conception of new applications for humanity to benefit. Indeed, in an interview dated on Sept 14, 2015, world renown University of California at Berkeley professor Alberto Sangiovanni-Vincentelli affirmed “In the near future, we are going to have a sensory swarm, a great deal, a great variety of all kinds of heterogeneous sensors that are going to interface the cyber world, the computing world, with the physical world”. And he added “Sensors will be immersed in the environment“.
All above and Alberto’s vision urges the research community to address the goal the TinyML working group set for On Device Learning (ODL) which is defined as follows: … to make edge devices “smarter” and more efficient by observing changes in the data collected and self-adjusting / reconfiguring the device’s operating model. Optionally the “knowledge” gained by the device is shared with other deployed devices.
In this talk, the technology vision mentioned above will be elaborated from a system and requirements point of view, some of its machine learning and statistical components will be discussed with specific reference to the algorithms and their implementation on micro- controllers. A common framework of approaches will be introduced since they are horizontal technologies that can be specialized for each of the above use cases. An example of an industry tool that helps to modelize these algorithms will be provided. Further readings will be provided to deepen the topic.
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