Endpoint AI – the Road to a Trillion Intelligent Endpoints

The world around us is becoming a whole lot smarter. We hear lots of predictions about how the Internet of Things (IoT), cloud computing, and other exciting technologies will change our lives in the next decades. IoT is enriching and transforming applications throughout our daily lives in industries from home and healthcare to smart cities, retail, and industrial. The application space is diverse with hundreds of sub-segments and thousands of applications. These applications generate a lot of data, but the data itself is not the important thing, it is the value we can extract from it. We cannot rely on the common approach of sending all the data back to servers in the cloud. As the data increases this cannot scale. We need a different solution.  

Transferring data from endpoints to the cloud introduces costs including longer latency, data transmission energy, bandwidth, and server capacity which in the end can make or break the value of a use case. This occurs especially in IoT, where there are many applications that rely on data analytics and decision making in real-time and at the lowest latency possible. In fact, sometimes endpoints might only have limited (if any) connectivity. As a result, intelligence has to be distributed across the network to make the right processing capabilities available in the right place, all the way to the endpoint at the source of the data. Increasing the on-device compute capabilities combined with machine learning techniques, has the potential to unleash real-time data analytics in the IoT endpoints – this convergence is also known as “Endpoint AI”.