ST Microelectronics has added neural network AI features to its STM32CubeMX ecosystem for edge and embedded devices.
“ST’s new neural-network developer toolbox is bringing AI to microcontroller-powered intelligent devices at the edge, on the nodes, and to deeply embedded devices across IoT, smart building, industrial, and medical applications,” said Claude Dardanne, President, Microcontrollers and Digital ICs Group, STMicroelectronics.
With STM32Cube.AI, developers can now convert pre-trained neural networks into C-code that calls functions in optimized libraries that can run on STM32 MCUs. The ready-to-use software function packs that include example code for human activity recognition and audio scene classification. These code examples are immediately usable with the ST SensorTile reference board and the ST BLE Sensor mobile app.
The STM32Cube.AI extension pack (part number: X-Cube-AI) can be downloaded inside ST’s STM32CubeMX MCU configuration and software code-generation ecosystem.
Today, the tool supports Caffe, Keras (with TensorFlow backend), Lasagne, ConvnetJS frameworks and IDEs including those from Keil, IAR, and System Workbench.
The FP-AI-SENSING1 software function pack provides examples of code to support end-to-end motion (human-activity recognition) and audio (audio-scene classification) applications based on neural networks. This function pack leverages ST’s SensorTile reference board to capture and label the sensor data before the training process. The board can then run inferences of the optimized neural network.
The ST BLE Sensor mobile app acts as the SensorTile’s remote control and display.
The toolbox consisting of the STM32Cube.AI mapping tool, application software examples running on small-form-factor, battery-powered SensorTile hardware, together with the partner program and dedicated community support offers a fast and easy path to neural-network implementation on STM32 devices.