MicroAI To Bring AI Training To Renesas MCUs
AI technology deployed at endpoint drives faster time to market for asset-intensive industries
MicroAI, the pioneer in edge-native artificial intelligence (AI) and machine learning (ML) products, announced that it has integrated its MicroAI AtomML technology with the Renesas RA Microcontroller (MCU) product line. The collaboration with Renesas, a global leader in microcontrollers, brings machine learning to MCUs and, with MicroAI, the ability to train machine learning models directly in the embedded environment—a first for the industry.
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Asset owners and manufacturers of industrial, commercial and consumer systems and devices are now able to quickly adopt Edge AI into their machines by utilizing the MicroAI-powered MCUs. This allows for intelligence to be embedded at the source of the data, enabling lower connectivity, cloud, and operational costs while expediting time to market for AI-powered solutions. Embedding MicroAI provides next generation intelligence for machines and IoT devices.
“We are excited to work with MicroAI to support its technology on our MCUs,” said Mohammed Dogar, senior director of global business development, Renesas. “The industry has been asking to bring more insight and intelligence into the performance of their assets closer to the source of the data, and, working with MicroAI, we have a solution.”
MicroAI is a sophisticated patented machine learning algorithm that lives directly on a machine or IoT device, providing asset owners and manufacturers with deep insight into the behavior, health and performance of their equipment/devices. For example, robotic welding arms across the automotive assembly lines or greenhouse gas efficiency in agriculture. Asset owners and manufacturers often face unexpected downtime and static maintenance schedules, which create unnecessary costs and avoidable service hours. Lack of visibility into asset performance means they can react only when a problem occurs.
By creating more visibility into the operations of manufacturing lines, specifically what is causing both unplanned downtime events and nuisance events, asset owners and manufacturers can make adjustments to reduce those events to keep operations running smoothly.
“Companies around the globe have been asking for predictive insight into how their assets are performing, behaving and being utilized to increase the productivity of the equipment they deploy,” said MicroAI chief executive officer Yasser Khan. “Working with Renesas, MicroAI is delivering that capability by utilizing our technology to bring machine learning to MCUs, providing the ability to train machine learning models directly in the embedded environment.”
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