ONE Tech redefines edge AI on MCUs
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Latest MicroAI product upgrade helps OEMs reduce costs, perform predictive maintenance
ONE Tech, creators of edge AI technology, has added new capabilities to its MicroAI Atom product, which is designed to be embedded on microcontroller units (MCUs) and can now train and run AI models directly at the endpoint. The first-of-its kind advancement enables silicon manufacturers, original equipment manufacturers (OEMs), smart device manufacturers and smart device owners to reduce the costs of bringing intelligence to the edge and endpoint by at least 80%.
MCU-based devices can now perform tasks at the network edge—such a predictive maintenance—that were previously only available on microprocessor units (MPUs). This new functionality enables manufacturers ranging from IoT devices/hardware to household appliances to industrial assets to deliver differentiated product offerings.
Groundbreaking phase for the industry
“This is a groundbreaking 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,” says Yasser Khan, CEO, ONE Tech. “Training and running a model on an MCU has not been seen before in the industry.”
“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,” Khan continues. “Companies in industries such as manufacturing needed this technology yesterday. It is the next evolution of IoT and AI at the network edge.”
Need for processing IoT data locally
A sea change has occurred in the IoT market over the past several years. 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 status quo for IoT deployments. This progression went from setting static thresholds that would trigger events when the threshold was met or exceeded to creating dynamic thresholds.
“AI models have been largely trained in GPU server environments in the cloud,” Khan says. “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.”
Webinar to cover topic in detail
MicroAI is a sophisticated machine learning multi-dimensional behavioural algorithm that runs recursive analysis. It is used by enterprises and OEMs worldwide as a tool to achieve deeper insights into the behaviour of devices, machines and processes within their organizations. 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.
ONE Tech will host a webinar on August 5 to cover this topic in more detail.