Edge Impulse Enables Machine Learning on Cortex-M Embedded Devices

Artificial intelligence used to happen almost exclusively in the cloud, but this introduces delays (latency) for the users and higher costs for the provider, so it's now very common to have on-device AI on mobile phones or other systems powered by application processors. But recently there's been a push to bring machine learning capabilities to even lower-end embedded systems powered by microcontrollers, as we've seen with GAP8 RISC-V IoT processor or Arm Cortex-M55 core and the Ethos-U55 micro NPU for Cortex-M microcontrollers, as well as Tensorflow Lite. Edge Impulse is another solution that aims to ease deployment of machine learning applications on Cortex-M embedded devices (aka Embedded ML or TinyML) by collecting real-world sensor data, training ML models on this data in the cloud, and then deploying the model back to the embedded device. The company collaborated with Arduino and announced support for the Arduino Nano 33 BLE Sense and other 32-bit Arduino boards last May. The solution supports motion sensing, computer vision, and audio recognition to detect glass breaking, hydraulic shocks, manufacturing defects, and so on.