Engineering Tiny Machine Learning for the Edge - InformationWeek

Edge is all about intelligence, but those smarts must be squeezed into ever tinier form factors. Developers of artificial intelligence (AI) applications must make sure that each new machine learning (ML) model they build is optimized for fast inferencing on one or more target platforms. Increasingly, these target environments are edge devices such as smartphones, smart cameras, drones, and embedded appliances, many of which have severely constrained processing, memory, storage, and other local hardware resources. The hardware constraints of smaller devices are problematic for the deep neural networks at the heart of more sophisticated AI apps. Many neural-net models can be quite large and complex. As a result, the processing, memory, and storage requirements for executing those models locally on edge devices may prove excessive for some mass-market applications that require low-cost commoditized chipsets.