tinyML Summit 2022: Sensors and ML: waking smarter for less

tinyML Summit 2022
Sensing Session
Sensors and ML: waking smarter for less
Abbas ATAYA, Director of Machine Learning and Software team, TDK InvenSense

Machine Learning at the Edge – where does the buzz stop and the value begin? If the edge is where the internet stops and the real world begins, sensors define the edge. By application, sensors convert physical phenomena into digital signals: data. What happens next is where ML is driving innovation.

Edge implies the connectivity of a node to a bigger system. The node has a sensor chip, created in a process optimized for cost, size, and power. Transmitting raw sensor data is resource-intensive. Converting the data into information or knowledge before transmission can greatly reduce the overall burden on the system. Machine learning is revolutionizing conversion. Nodes also have a radio chip for connectivity, similarly optimized, though radio chips are made in more advanced process nodes. As such, the radio chip will always have more resources than the sensor chip. The HW/SW node design must balance the conversion giving each chip a clearly defined role to improve accuracy and power conservation in the application.

I will explore how machine learning creates new opportunities to partition far reaching sensor systems and greatly reduce required resources such as dollars, volume, and watts. Using examples of systemic optimization studied in our lab, I will show how phasing the wake up of the nodes in the hierarchy of the system reduces resources while improving response. The analogies we discuss are similar to a person waking up in the night in response to a noise, a smell, a light, or a vibration.

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