Artificial-intelligence (AI) solutions have taken the embedded community by storm, including high-performance solutions in the cloud. On the edge, inclusion of machine learning (ML) becomes more challenging because of size, weight, and power (SWaP) constraints, but this doesn’t force AI/ML solutions to be tied to the cloud. There are many options for AI/ML on the edge. However, it can be a challenge to know what’s available and how powerful a solution can fit within the desired design constraints.
We have gathered a group of experts who will discuss embedded AI/ML from microcontrollers to dedicated, high-performance solutions that can address everything from smart motor control to robotics and self-driving cars.