Kate Stewart works with the safety, security and license compliance communities to advance the adoption of best practices into embedded open source projects. Kate was one of the founders of SPDX, and is currently the specification coordinator. Since joining The Linux Foundation, she has launched the ELISA and Zephyr Projects, among others, and oversees other embedded projects hosted by the Linux Foundation. With over 30 years of experience in the software industry, she has held a variety of roles and worked as a developer in Canada, Australia, and the US and for the last 20 years has managed software development teams in the US, Canada, UK, India, and China. She received her Master's in computer science from University of Waterloo and Bachelor's of computer science from the University of Manitoba.
Over the last few years, we're starting to see machine learning be more effectively deployed closer to where data is collected in embedded systems. These end point devices may be resource constrained though, either in terms of power, memory or communication capabilities - sometimes all three. Being able to apply machine learning on these end point devices is possible, and enables system-wide efficiencies to be realized. This talk will explore the requirements and tradeoffs for such systems to be considered when using the Zephyr RTOS and Tensorflow Lite for Embedded Microcontrollers projects.