STMicroelectronics has extended its STM32CubeMX ecosystem by adding advanced Artificial Intelligence (AI) features.  AI uses trained artificial neural networks to classify data signals from motion and vibration sensors, environmental sensors, microphones and image sensors, more quickly and efficiently than conventional handcrafted signal processing. 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.
STM32Cube.AI comes together with 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. Additional support such as engineering services is available for developers through qualified partners inside the ST Partner Program and the dedicated AI and Machine Learning (ML) STM32 online community. ST will demonstrate applications developed using STM32Cube.AI running on STM32 MCUs this week in a private suite at CES, the Consumer Electronics Show, in Las Vegas, January 8-12.

The STM32Cube.AI extension pack 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 comprehensive 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.

STMicroelectronics | www.st.com