What is Edge AI and How Does It Create a New Paradigm
Artificial intelligence and machine learning (AI/ML) have been around for a while now. Without knowing the intricacies, people still understand that it has powerful uses, such as analyzing data, understanding pictures, or chatting with Siri. The edge is a much newer concept, which I first heard of with the development of 5G.
The edge is data processing with round trip servicing under 20 milliseconds. What does this actually mean? A more practical definition is self-explanatory. The edge is literally the closest point you can put your processing to where you need the result. This leaves a broader concept of edge. If your processing must be done in the cloud, then your edge is simply the closest/fastest servers. If your processing can be done locally, then your edge should be your local processor (smartphone?).
But what do you get when you marry the AI and edge concepts? At one point of time, AI algorithms became practical to implement in the cloud, running on powerful server racks. Since AI was too complex for local devices, the cloud AI was also the edge AI. This is no longer true. Powerhouses such as #arm and #NVIDIA have been making massive breakthroughs in optimizing AI performance. Startup #Hailo has developed their own AI processor. #STM has even put ML cores on individual sensors. AI can now run on embedded processors, sensors, internet gateways, security cameras, etc. The new edge AI is essentially anywhere.
Believe it or not, this new reality is a great thing. Since data is generated and processed locally, the edge AI device does not need to be connected to the internet. This means you can have smart devices that protect your privacy. You can have security systems that cannot be taken down by cutting the internet. AI analytics in remote areas? Got it. Edge enables AI to be anywhere it is useful, when it is useful.