New eyes for the IoT - [Opinion]
IEEE Spectrum · 2018
THE RISE OF COMPUTER VISION has given us robot chefs and cameras that detect gas flares in fuel production. It's also led to an increase in connected cameras that are trying to run at the edge of the network. · "Running at the edge" means these cameras are not only communicating wirelessly with the cloud but also communicating with local gateways and working with built-in logic boards to complete a task. The task might be as simple as notifying a manufacturer when a production line produces a defective item or as complex as identifying a person to determine if the system should sound an alarm. · But as we connect more cameras and ask them to perform more complicated tasks, their fundamental architecture is changing. Today we see changes in the silicon that handles image processing and computing. In a few years, we may see our notion of cameras change to meet the needs of digital eyes, not human ones. · There are two challenges driving the silicon shift. First, processing power: Many of these cameras try to identify specific objects by using machine learning. For example, an oil company might want a drone that can identify leaks as it flies over remote oil pipelines. Typically, training these identification models is done in the cloud because of the enormous computing power required. Some of the more ambitious chip providers believe that in a few years, not only will edge-based chips be able to match images using these models, but they will also be able to train models directly on the device.