Machine Learning on Resource Contained Devices
The Internet of Things has become pervasive in our daily home and work environments like factories, agriculture, mining, etc. IoT devices are growing so fast because they provide the data necessary for insights on how to do things smarter and more efficiently.
Collecting data from IoT end point devices enables more intelligent decision making. However a lot of these end point devices are resource constrained, 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 system to be considered when using the Zephyr RTOS and Tensorflow Lite for Embedded Microcontrollers projects.