Zach Shelby, Co-founder and CEO of Edge Impulse, presents the “How Do We Enable Edge ML Everywhere? Data, Reliability and Silicon Flexibility” tutorial at the May 2022 Embedded Vision Summit.
In this talk, Shelby reveals insights from the company’s recent global edge ML developer survey, which identified key barriers to machine learning adoption, and shares the company’s vision for how the industry can overcome these obstacles. Unsurprisingly, the first critical obstacle identified by the survey is data. But the issue isn’t simply a lack of massive datasets, as is often assumed. On the contrary, the biggest opportunities in ML will be enabled by highly custom, industry-specific and even user-specific data. We need to master data lifecycle and active learning techniques that enable developers to move quickly from “zero to dataset.”
The real and perceived inability of today’s ML algorithms to reach the ultra-high accuracy needed in industrial systems is another key barrier. New techniques for explainable ML, better testing, sensor fusion and model fusion will increasingly allow developers to achieve industrial-grade reliability. Finally, in order to accelerate ML adoption in embedded products, we must recognize that most developers can’t immediately upgrade their systems to use the latest chips — a problem that is compounded by today’s chip shortages. To enable ML everywhere, we have to find ways to deploy ML on today’s silicon, while ensuring a smooth transition to new devices with AI acceleration in the future.
See here for a PDF of the slides.
