The ever-advancing field of machine learning has created new opportunities for deploying devices and applications that leverage neural network inferencing with never-before-seen levels of vision-based functionality and accuracy. But, the rapidly evolving field has given way to a confusing landscape of processors, accelerators, and libraries. This article treats open interoperability standards and their role in reducing costs and barriers to using inferencing and vision acceleration in real-world products.
