Nathan Kopp, Principal Software Architect for Video Systems at the Chamberlain Group, presents the “Practical Guide to Implementing ML on Embedded Devices” tutorial at the May 2021 Embedded Vision Summit.
Deploying machine learning onto edge devices requires many choices and trade-offs. Fortunately, processor designers are adding inference-enhancing instructions and architectures to even the lowest cost MCUs, tools developers are constantly discovering optimizations that extract a little more performance out of existing hardware, and ML researchers are refactoring the math to achieve better accuracy using faster operations and fewer parameters.
In this presentation, Kopp takes a high-level look at what is involved in running a DNN model on existing edge devices, exploring some of the evolving tools and methods that are finally making this dream a reality. He also takes a quick look at a practical example of running a CNN object detector on low-compute hardware.
See here for a PDF of the slides.
