Chris Kim

Presenter Bio

Min Kyun (Chris) Kim was born in Seoul, South Korea. He received his B.S. in Electrical Engineering at the University of Illinois at Urbana-Champaign (2016). He joined UICO in 2016 as an Embedded Hardware and Firmware Engineer, where he designed proprietary hardware and firmware for PCAP touch screens that work under heavy rain and with thick winter gloves. He then joined STMicroelectronics in 2020 as Senior Application Engineer specializing in MEMS sensors and is also currently pursuing M.S. in Computer Science at Georgia Institute of Technology with specialization in Interactive Intelligence.

Innovative intelligence in MEMS Inertial Sensors
The latest generation of MEMS inertial sensors are equipped with innovative machine learning and finite state machine capabilities.  These embedded features enable efficient sensor data computation at ultra-low power consumption which is critical to many battery-operated sensor nodes in a wide range of applications. Machine learning processing capability at the edge allows for moving computations/algorithms from host processor directly into the inertial sensor, leading to an easy implementation and more accurate inertial sensor data processing with low latency. This presentation provides a technical deep dive into the above-mentioned innovative processing capabilities, their sensor level architecture, and the key advantages.

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