Over the last few years, single board computers have experienced a massive transition because an 8-bit CPU has now become a small-sized quad-core machine with unlimited RAM. This shows how edge-computing is growing and it might be tempting to know how the SBCs will benefit. Single-board computers are being integrated with artificial intelligence, and this is happening increasingly because of the benefits it brings forth. With AI, customizable products can be created, and the same can be improved to suit the demands of all the customers. A solution meant to help certain individuals can be customized to better the experiences of many other clients, thanks to AI.

Transforming AI into a product requires the cloud-based AI systems, and this can be troublesome being the only reliable method for implementing AI. Since the main algorithm runs at a given data center, there are robust issues during integration when the customers send and receive information.

Privacy with AI has become a sensitive issue because the information is being disseminated to an unknown location, and even end up in the hands of unauthorized people. The fact that the user’s questions are first sent to the data center for AI processing, proves how the details will later be accessed by other people. A good example is the Alexa-based case from Amazon.

Also, latency will occur when a small delay is caused by the products with a remote data center since there is no instant internet connectivity as they send data and wait for the processing to get the results. Latency depends on traffic, and the products will be more unresponsive with the increase in the number of internet users.

All the challenges experienced above can be solved through edge-computing because it is an all-around concept. Issues to do with privacy concerns, overreliance on internet access, and provision of on-device algorithms that do not depend on a given data center is the main concern. Edge computing entails processing the data on the edge of the network, and here there are no internet devices below it. This is the perfect approach because it deals with all the cloud-based challenges many AI systems have.

Edge computing can as well counter latency bearing in mind that AI execution is shifted from a data center to the device, and so the AI neural networks are processed when data is available. Therefore, edge computing ensures that all takes place in the device, minimizing the prior process that requires an internet connection, and so reducing latency. The private information for the device is safe and intact with edge computing.

AI co-processors are being developed to run the algorithms and neural-net, and this gives the main process a chance to perform the other tasks. Therefore, there are some options available for edge computing that will facilitate single board computers.

The Google Coral range has a co-processor meant for mobile and other embedded applications that executes the AI algorithms, and it mainly serves the small devices. It has a Dev board suitable for edge computing in several AI environments mostly in the areas where internet connectivity is limited.

Nvidia Jetson Nano suits the AI cases that need several neural networks to run in parallel, and at the same time keeping the energy consumption low. For only 5W, simultaneous neural networks for applications like object detection as well as speech processing can run on an SBC.

The Intel Stick can turn all the HDMI displays into computers, and it is the smallest SBC currently. The physical size of the SBC is not even comparable to a pack of gum, but it has 4GB of RAM as well as 64 GB storage and can integrate various forms of connectivity like Wi-Fi, Bluetooth, and even three USB ports.

Joyce Laroche