AI at the Far Edge

The concept of "edge computing" has been around since the late 90s, and typically refers to systems that process data where it is collected instead of having to both store and push it to a centralized location for off-line processing. The aim is to move computation away from the data center in order to faciliate real-time analytics and reduce network and response latency. But some applications, particularly those that leverage deep learning, have been historically very difficult to deploy at the edge where power and compute are typically extremely limited. The problem has become particularly accute over the past few years as recent breakthroughs in deep learning have featured networks with a lot more depth and complexity, and thus require greater compute from the platforms they run on. But recent developments in the embedded hardware space have bridged that gap to a certain extent and enable AI to run fully on the edge, ushering a whole new wave of applications.