With billions of connected devices throughout the world, the Internet of Things has advanced tremendously in recent years. However, more work is needed in various areas for IoT to truly deliver on its promise. One is to increase the speed with which data is processed in order to provide real-time intelligence. Allow me to explain. IoT is creating vast volumes of data, and more is being added to the mix every day. At the start of 2020, the Globe Economic Forum anticipated that the world would have 44 zettabytes of data.
In 2025, the quantity of data generated each day is expected to exceed 463 exabytes, according to the same organisation. Sensor processing becomes considerably more complex as we travel into space. Because of the lack of high-speed communications routes and limited storage, satellites may flush data every day, whether or not it is used. In many cases, cloud data centres are used to store and process IoT data.
What are the types of real-time applications I’m referring to? Consider having an automated customs line with access to a database of all passports. For example, in a hospital institution where facial recognition is used to allow medical staff access while restricting patients for their own safety. CPUs have long been the backbone of data processing. GPUs have lately joined the scene, speeding up training in order to extract more patterns from data.
Even with GPUs, though, one old challenge persists: quick inference at the edge. In AI, training comprises instructing the system on how to carry out a specific task. Inference refers to the AI’s capacity to apply what it has learned to the task at hand. GPUs are ideal for data training. On big swathes of data, a lot of parallel multiplying accumulates. Inference, on the other hand, is a single query that involves using the trained model to make key judgments in real time.