Applications
Transforming industries and challenges with Edge-AI
SOURCE: BECKHOFF
Analog compute-in-memory takes advantage of high-density flash memory to enable compact, single chip processor designs. It dramatically reduces power consumption and, by reducing the barriers of cost, size, power and performance, companies can truly rethink what’s possible with AI.
New AI technology companies are removing hurdles with implementing artificial intelligence in industrial automation applications by providing a complete inference solution – high performance and low-power hardware in a small form factor, along with ready-to-deploy DL algorithms. TODAY COMPANIES HAVE ONLY SCRATCHED THE surface of what AI is capable of at the edge. The full potential of AI is being constrained by a number of different factors. One challenge has been the limited compute capabilities of edge devices, at least until recently. Additionally, edge inference, what enables a neural network to perform different functions when encountering real-world data, has been a major technical challenge. The good news is that advancements in edge-AI processing are working to solve these challenges and open up a new world of AI-powered possibilities. Let’s take a look at the top five industries that will be transformed by edge-AI.
Industrial Automation
Over the past few decades, factories have become more and more automated, bringing efficiency to new heights and driving down the
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costs of the goods that consumers use every day. While factories have already deployed computer vision (CV) to optimize production lines, factories can now take advantage of new edge-AI processing technologies to combine the power of CV with AI, especially deep learning (DL). This will significantly improve factory throughput and quality, two essential metrics for an efficient production line. As factories become more automated with interactive human-to-machine processes, AI is also being used to deliver a new level of workplace safety. Just imagine a food processing factory with AI-powered collaborative robots to inspect cereal boxes on the production line in real time. Deploying AI solutions for industrial automation is a challenge. Automation engineers in the manufacturing field don’t
have the expertise to develop effective AI/DL algorithms. However, several AI technology companies are removing these hurdles by providing a complete inference solution – high performance and low-power hardware in a small form factor, along with ready-to-deploy DL algorithms. We’ll see more investment being poured into this area as more factories want to take advantage of powerful edge-AI processing to improve efficiency and workplace safety.
Aerospace/Drones
Edge-AI processing is also enabling many different types of physical surveillance applications for drones. Drones are being outfitted with ultra-high definition cameras that can be used for many CV applications, including monitoring agricultural yields, inspecting critical infrastructure such as
in d u s t r ial et h er ne t b o o k
07.2021
