It is becoming apparent that some AI functions should be performed at the edge of the system in order to avoid the need for the power to run artificial intelligence because it consumes so much energy and is using it so rapidly.
Drew Robb, writing for TechRepublic Premium, explains why some tasks need to move downward, what AI at the top is, the benefits, the issues, and how it can get accomplished.
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WHY EDGE AI?
There are many causes why AI needs to accomplish some level of devolution:
Power: AI takes more energy than can easily be fed to and integrated into large, unified data centres. Either the native grid’s energy is insufficient to meet the needs of an AI data center, or the data center lacks the necessary underlying power infrastructure to support fully developed AI applications.
Cooling: Even if the power can be brought in to satisfy AI applications, many existing data centers would n’t be able to cool the servers and processors. Interruptions may be expected due to overheating. Liquid cooling has been proposed as the solution, but many data centers either do n’t have room to retrofit it, do n’t have the skilled manpower to support it, or ca n’t justify it economically.
Overhead: If you send all data to and execute all evaluation at a key point, you introduce circular trip overhead. When the data center is located hundreds or thousands of miles away, significant time is wasted as the numbers are crunched and the information is moved around. This is especially a component with real-time software. Think self-driving cars being lulled by a minute after each choice. If the vehicle is traveling at more than about 20 miles per hour ( 32.19 kilometres per hour ), crashes and accidents would be commonplace.
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