Harnessing the power of AI
Artificial intelligence, or AI, is everywhere. It’s a revolutionary technology that is slowly pervading more industries than you can imagine. It seems that every company, no matter what their business, needs to have some kind of AI story.
In particular, you see AI seriously pursued for applications like self-driving automobiles, the Internet of Things (IoT), network security, and medicine. Company visionaries are expected to have a good understanding of how AI can be applied to their businesses, and success by early adopters will force holdouts into the fray.
Not all AI is the same, however, and different application categories require different AI approaches. The application class that appears to have gotten the most traction so far is embedded vision. AI for this category makes use of so-called convolutional neural networks, or CNNs, which attempt to mimic the way that the biological eye is believed to operate. We will focus on vision in this AI whitepaper, even though many of the concepts will apply to other applications as well.
AI edge requirements
AI involves the creation of a trained model of how something works. That model is then used to make inferences about the real world when deployed in an application. This gives an AI application two major life phases: training and inference.
Training is done during development, typically in the cloud. Inference, on the other hand, is required by deployed devices as an ongoing activity. Because inference can also be a computationally difficult problem, much of it is currently done in the cloud. But there is often little time to make decisions. Sending data to the cloud and then waiting until a decision arrives back can take time – and by then, it may be too late. Making that decision locally can save precious seconds.
This need for real-time control applies to many application areas where decisions are needed quickly.
Because of this need for quick decisions, there is a strong move underway to take inference out of the cloud and implement it at the ‘edge’ – that is, in the devices that gather data and then take action based on the AI decisions. This takes the delays inherent in the cloud out of the picture.
There are two other benefits to local inference. The first is privacy. Data enroute to and from the cloud, and data stored up in the cloud, is subject to hacking and theft. If the data never leaves the equipment, then there is far less opportunity for mischief.
The other benefit relates to the bandwidth available in the internet. Sending video up to the cloud for real-time interpretation chews up an enormous amount of bandwidth. Making the decisions locally frees that bandwidth up for all of the other demanding uses.
Download the full whitepaper to read more.