STM32Cube.AI converts pretrained neural networks into optimised C code for STM32 microcontrollers (MCUs) and is an essential tool for developing cutting-edge AI solutions that make the most of the constrained memory sizes and computing power of embedded products. Moving AI to the edge, away from the cloud, delivers substantial advantages to the application. These include privacy by design, deterministic and real-time response, greater reliability, and lower power consumption. It also helps optimise cloud usage.
Now, with support for deep quantisation input formats like qKeras or Larq, developers will be able to reduce network size, memory footprint, and latency. These benefits support new possibilities from AI at the edge, including frugal and cost-sensitive applications and, as a result, developers will be able to create edge devices, such as self-powered IoT endpoints that deliver advanced functionality and performance with longer battery runtime. ST’s STM32 family provides many suitable hardware platforms. The portfolio extends from ultra-low-power Arm Cortex-M0 MCUs to high-performing devices leveraging Cortex-M7, -M33, and Cortex-A7 cores.
STM32Cube.AI version 7.2.0 also adds support for TensorFlow 2.9 models, kernel performance improvements, new scikit-learn machine learning algorithms, and new Open Neural Network eXchange (ONNX) operators.
