AmI refers to edge devices that are sensitive and responsive to the presence of people. Intensive care is an area that could benefit from edge-based ML, where real-time data processing and decision making are important for closed-loop systems that must maintain critical physiological parameters, such as blood glucose level or blood pressure, within specific range of values.Īs the hardware and machine learning methods become more sophisticated, more complex parameters can be monitored and analyzed by edge devices, like neurological activity or cardiac rhythms.Īnother area that may benefit from edge-based data processing is “ambient intelligence” (AmI). For example, there may not be sufficient time for data to be transferred to back-end servers, or there is no connectivity at all. The need for on-device data analysis arises in cases where decisions based on data processing have to be made immediately. The following sections focus on industries that will benefit the most from edge-based ML and existing hardware, software, and machine learning methods that are implemented on the network edges. This has given rise to the era of deploying advanced machine learning methods such as convolutional neural networks, or CNNs, at the edges of the network for “edge-based” ML.

However, with the arrival of powerful, low-energy consumption Internet of Things devices, computations can now be executed on edge devices such as robots themselves. Therefore, much processing of data takes place in on-premises data centers or cloud-based infrastructure. Source: IntelĪnalyzing large amounts of data based on complex machine learning algorithms requires significant computational capabilities.

Intel’s Neural Compute Stick 2 is an example of machine learning hardware for edge devices.

Machine learning algorithms ROBOTC