MicroAI integrates with Renesas MCUs for machine learning

Asset owners and manufacturers of industrial, commercial and consumer systems and devices are now able to quickly adopt edge AI into their machines by utilising 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 patented machine learning algorithm that lives directly on a machine or IoT device, providing asset owners and manufacturers with deep insight into the behaviour, health and performance of their equipment or 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 that 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 utilised 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 utilising our technology to bring machine learning to MCUs, providing the ability to train machine learning models directly in the embedded environment.”