MicroAI AtomML™ Brings AI Down to the Device MCU Level
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Using MicroAI AtomML™ to Enable Endpoint AI Solutions

Endpoint AI Solutions

Using MicroAI AtomML™ to Enable Endpoint AI Solutions

The Business Need 

Many companies are considering Artificial Intelligence (AI) solutions to monitor and manage their smart assets. These deployments provide greater understanding and deeper insights that are not obvious to the human eye. However, AI solutions often require complex and expensive hardware to perform the training processes needed to create the necessary AI models. This hardware is typically not available in a local environment, and thus the expensive task of sending and processing large amounts of data in the cloud is almost always required. These additional costs and challenges have turned many developers away from AI-enabled solutions.

The Solution 

The logical advancement in this technology would be to deploy AI at the edge; however, MicroAI has developed MicroAI AtomML™, an AI solution that lives on endpoint microcontroller (MCU) based devices.  Endpoint-based AI provides many potential advantages that are not present in traditional edge or cloud-based solutions. Typical benefits would include the following:

  • Training models are designed with more individuality and therefore are more specific and relevant to the specific application.
  • Data is not processed at its source. This results in reduced latency between collection and training or execution of AI models.
  • A reduction in the amount of data being sent from endpoint to edge device since the data processing has already occurred.
  • Real-time asset management and optimization driven by local AI results.
  • More intimate security enablement to protect valuable assets from cyber-attack.
  • Compact enough to run on any microcontroller unit, bringing AI training to the individual device or machine.



The Impact 

Deployment of MicroAI’s MicroAI AtomML™ provides tangible advantages to any company that utilizes smart assets or machines as part of their operation. Operational advantages include the following:

  • Ability to enable AI-training while avoiding the costs associated with additional hardware and cloud-based data storage.
  • A more intimate approach to asset management that facilitates faster problem detection, notification, and resolution.
  • Smart assets continue to evolve through a process of self-learning and self-correction.
  • The local approach to device security provides enhanced protection against threats to the IoT ecosystem.
  • Improved operational flexibility. Deploy AI on a single asset MCU or deploy across an entire asset ecosystem.