The next frontier for industrial digitization and automation is the convergence of artificial intelligence (AI) and machine vision.
AI-powered machine vision promises to transform the way industrial manufacturers conduct their business, according to experts at a recent webinar hosted by the Association for Advancing Automation (A3). The webinar, entitled Harnessing AI-Powered Machine Vision for Industrial Success, brought together industry leaders to discuss how those two tools opens up many possibilities for industrial companies to maximize their competitiveness, from improving quality control to enhancing safety to optimizing production processes.
Michael Kleiner, VP of Edge AI Solutions at OnLogic; Prateek Sachdeva, Co-Founder and Chief Product Officer at Invisible.AI; and Gareth Powell, Product Marketing Director at Prophesee, discussed the potential of AI-powered machine vision to help industry optimize their operations and compete in a global economy.
Converging trends lower the bar for deploying AI in industrial settings
Kleiner identified out two trends facilitating the use of machine vision in industrial digital transformation: more powerful CPUs and improvements in model compression.
“We’re seeing a real increase in the AI capabilities of what’s within the CPU package,” siad Kleiner. This is particularly true in x86 CPU architectures which are commonly used in industry. This growth is taking place largely thanks to the inclusion of AI-optimized GPU hardware within the physical CPU. More and more internal GPUs (iGPUs) are being installed; these architectures are better suited to handle AI workloads than a conventional CPU. More recently, neural processing units (NPUs) have been added as well.
“With these additions, in terms of AI power, it’s growing faster than linear, more according to Moore’s Law, which is really helpful if we want to do AI at the edge,” said Kleiner. “We’re seeing a good amount of growth; adding these architectures and optimizing them for AI workloads is giving us more power.”
Along with more powerful CPUs, techniques to compress machine learning models have also been improving, enabling CPU architectures to do more with the data than ever before. These compression techniques include model choice, quantizing/data types, pruning/sparsity optimization, knowledge distillation, low rank factorization technique, and more.
This improvement is driven by the sheer increase in memory size needed to operate those models: the models need to be better compressed to bring them down to a manageable size for the CPU’s compute power—which is particularly beneficial for CPUs at the edge, which face significant resource constraints when running AI programs. “Increasing compute power withing the CPU package, and model compression so we can do more with a given architecture, really helps enable a growing number of AI use cases that can handle the inference tasks with the computational power of what’s in the CPU,” said Kleiner.
The convergence of these trends means a reduction in the physical complexity required by edge systems, more options in compute hardware because specialized high-end systems won’t me needed, as well as efficiencies in power use and emissions. “All of this helps to simplify processing data in real-time at the edge and lowers the barrier of entry for many AI deployments, including machine vision,” said Kleiner.
Deploying AI-powered sensors at the edge
Sachdeva says devices that capitalize on that convergence can help industry. His company, Invisible.AI, has developed an intelligent camera for manufacturing, and a software platform that monitors and learns from the camera’s recordings, to deliver insights for process optimization, safety and continuous improvement.
“Manufacturing changes every single day, every week, and you’re conducting optimization on the line,” said Sachdeva. “To be able to do data collection for every scenario is just not practical. Your solution with AI needs to work quickly, needs to be able to deploy day one, week one, not weeks or months from now, and not depend on a lot of data collection.”
Pour lire l'article complet : All roads lead to vision for AI-powered industrial processes | Engineering.com
