The collaboration brings machine learning to MCUs and, with MicroAI, the ability to train machine learning models directly in the embedded environment, said to be a first for the industry.
Users will be able to adopt Edge AI into their machines by utilising the MicroAI-powered MCUs, and this will allow 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 will provide 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 users with deep insight into the behaviour, health and performance of equipment and 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, they will be able to make adjustments to reduce those events to keep operations running smoothly.
