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.
Artificial Neural Networks on General Purpose Microcontrollers
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.
Artificial Neural Networks on Automotive Microcontrollers
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
"A grapevine leaves dataset for early detection and classification of Esca disease in vineyards through machine learning", M. Alessandrini, R. C. Fuentes Rivera, L. Falaschettia, D. Pau, V Tomaselli, C Turchetti; Data in Brief, Jan 2021
Deep Learning Localization with 2D Range Scanner; Giuseppe Spampinato, Arcangelo Ranieri Bruna, Ivana Guarneri, Davide Giacalone - 2021 International Conference on Automation, Robotics and Applications (ICARA 2021)
Low Cost Point to Point Navigation System; Giuseppe Spampinato, Arcangelo Ranieri Bruna, Davide Giacalone, Giuseppe Messina - 2021 International Conference on Automation, Robotics and Applications (ICARA 2021)
A Deep Learning Short Commands Recognition for MCU in robotics applications; Ivana Guarneri, Giuseppe Messina, Arcangelo Bruna, Davide Giacalone - 2021 International Conference on Automation, Robotics and Applications (ICARA 2021)
Characterization of Neural Networks Automatically Mapped on Automotive-grade Microcontrollers; Danilo Pau, Davide Denaro, Luigi Zambrano, Giuseppe Di Giore (STM); Giulia Crocioni; Gian Battista Gruosso (Politecnico di Milano) - First International Research Symposium on Tiny Machine Learning (TinyML 2021)
The full list of papers is available here