Artificial Intelligence

Artificial Intelligence

STM32 solutions for Artificial Neural Networks

Artificial intelligence (AI) is a set of hardware and software systems capable of providing computing units with capabilities that, to a human observer, seem to imitate humans’ cognitive abilities.

It uses an assembly of nature-inspired computational methods to approximate complex real-world problems where mathematical or traditional modeling have proven ineffective or inaccurate. Artificial Intelligence uses an approximation of the way the human brain reasons, using inexact and incomplete knowledge to produce actions in an adaptive way, with experience built up over time.

ST has been actively involved in AI research for many years and has applied its knowledge to develop tools that allow embedded developers to take advantage of AI techniques on ST microcontrollers and sensors. 

AI at the Edge

Artificial Neural Networks (ANNs) address a variety of problems which occur in everyday life. They can exploit the data provided by sensors present in our environments, homes, offices, cars, factories, and personal items. A widespread model assumes the raw data from sensors are sent to a powerful central remote intelligence (Cloud), thus requiring significant data bandwidth and computational capabilities. That model would lower responsiveness if you consider the processing of audio, video or image files from 100s millions of end devices.

Switching from a centralized to a distributed intelligence system

AI enables much more efficient end-to-end solutions when the analysis done in the cloud is moved closer to the sensing and actions. This distributed approach significantly reduces both the required bandwidth for data transfer and the processing capabilities of cloud servers, leveraging modern computing capabilities at the edge. It also offers user data sovereignty advantages, as personal source data is pre-analyzed and provided to service providers with a higher level of interpretation.

processing capabilities of cloud servers


Artificial Neural Networks on General Purpose Microcontrollers

Artificial Intelligence @ ST

Thanks to ST’s new set of Artificial Intelligence (AI) solutions, you can now map and run pre-trained Artificial Neural Networks (ANN) using the broad STM32 microcontroller portfolio.

Contact us at to find out more on how you can run edge AI applications on STM32 microcontrollers and application processors.

Artificial Neural Networks on Automotive Microcontrollers

Artificial Intelligence @ ST

Thanks to ST’s SPC5Studio.AI component for our fully customizable SPC5Studio Eclipse development environment, you can now convert, analyze and deploy automotive neural network models on our SPC58 Chorus automotive microcontrollers.

Machine Learning on Sensors


Advanced sensors, such as the LSM6DSOX (IMU), contain a machine learning core, a Finite State Machine (FSM) and advanced digital functions to provide to the attached STM32 or application central system capability to transition from ultra-low power state to high performant high accuracy AI capabilities for battery operated IoT, gaming, wearable technology and consumer electronics.


Latest news about Artificial Intelligence



ST-Published papers on Artificial Intelligence



Tiny Neural Network Pipeline for Vocal Commands Recognition @Edge; Ivana Guarneri, Alessandro Lauria, Giovanni Maria Farinella,Corrado Santoro, HUCAPP 
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A quantitative review of automated neural search and on-device learning for tiny devices; Danilo Pau, Prem Kumar Ambrose, Ital-AI
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Hybrid Quantization for Vocal Commands Recognition on Microcontroller; Ivana Guarneri, Viviana D'Alto, Danilo Pau, Marco Lattuada, TinyML Summit
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Tiny decomposition of complex neural networks for heterogeneous microcontrollers; Biagio Montaruli , Andrea Santamaria , Danilo Pau, TinyML Summit
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Complexity bounded classification of fisheye distorted objects with micro-controllers; Danilo Pau, TinyML Summit
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Deep open-set recognition for silicon wafer production monitoring; Luca Frittoli, Diego Carrera, Giacomo Boracchi, Pattern Recognition Journal
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The full list of papers is available here

All previous papers by year are available in PDF version.: