As AI moves towards the Edge and demand for intelligent Edge grows, there is more pressure on developers to move faster. However, they are struggling with the challenge of fitting powerful models onto tiny microcontrollers and face a steep learning curve.
Recognising this challenge, ADI co-developed AutoML for Embedded to make Edge AI accessible, efficient and scalable for everyone.
AutoML for Embedded simplifies the process by automating the end-to-end machine learning pipeline, enabling developers without data science expertise to build high-quality and efficient models that deliver robust performance.
In a recent demonstration, the tool was used to create an anomaly detection model for sensory time series data on the ADI MAX32690 MCU. The model was deployed both on physical hardware and its digital twin in Renode simulation, showcasing seamless integration and real-time performance monitoring.
