MicroAI Atom is designed to be embedded on microcontroller units (MCUs) and can now train and run AI models directly at the endpoint. This advancement enables silicon manufacturers, original equipment manufacturers (OEMs), smart device manufacturers and smart device owners to significantly reduce the costs of bringing intelligence to the edge and endpoint.
MCU-based devices can now perform tasks at the network edge, that were previously only available on microprocessor units (MPUs) and the new functionality will enable manufacturers to deliver differentiated product offerings.
“This is a ground-breaking phase for the industry. By bringing intelligence to endpoints, sensors and equipment at the network edge, device and equipment manufacturers, along with the owners of these assets, can now have AI-driven intelligence on a low-cost piece of hardware. Training and running a model on an MCU has not been seen before in the industry,” said Yasser Khan, CEO, ONE Tech. “AI is shrinking and can run these advanced algorithms. It allows AI and predictive maintenance to move from MPU-based devices to MCU-based devices, with a small footprint and significantly lower price point. Companies in industries such as manufacturing needed this technology yesterday. It is the next evolution of IoT and AI at the network edge.”
Early iterations of IoT solutions primarily consisted of deploying sensors that would pull IoT data points for monitoring assets that the sensors were attached to. This resulted in an influx of data that needed to be further processed and acted upon. Now the need for processing IoT data locally to enable automated action is becoming essential for IoT deployments.
“AI models have been largely trained in GPU server environments in the cloud,” said Khan. “With ONE Tech’s MicroAI, the early versions were designed to train and run in MPU environments. Today, ONE Tech is delivering the ability to train and run AI Models directly in MCU environments.”
MicroAI is a sophisticated machine learning multi-dimensional behavioural algorithm that runs recursive analysis. It is used as a tool to achieve deeper insights into the behaviour of devices, machines and processes. MicroAI lives directly on a targeted machine or IoT device and allows enterprises to reduce unexpected downtimes associated with maintenance issues and cyberattacks.
Unlike traditional AI solutions that originate or reside only in the cloud, MicroAI is configured and trained on the network edge. This enables real-time analytics and alerts that optimize asset performance, increase security and privacy, and improve visibility and worker safety.
