Know the Edge AI Ecosystem

Successful adoption of Edge AI requires understanding and integrating different elements in a way that this stack can be seamlessly deployed in the target environment. Implementing an Edge AI application requires an understanding of aspects like the tasks to be performed, hardware, frameworks, and models. For deep neural networks to run at the edge; hardware, frameworks, and tools need to work collectively. As edge AI applications vary according to the use case, these requirements need to be thought through for each of the scenarios. It is necessary to select proper hardware, frameworks, and tools that will be compatible with each other and the best suited for the use case. Below we discuss briefly a few of the frameworks, hardware processors, and development boards.