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.
Contact us at email@example.com to find out more on how you can run edge AI applications on STM32 microcontrollers and application processors.
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
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ST-Published papers on Artificial Intelligence
Intelligent Embedded Load Detection at the Edge on Industry 4.0 Powertrains Applications. S. Akhtari, F. Pickhardt, D. Pau, A. Di Pietro, G. Tomarchio - 2019 IEEE 5th International forum on Research and Technology for Society and Industry (RTSI), September 2019
Environmental Intelligence for Embedded Real-time Traffic Sound Classification. P. Montino, D. Pau - 2019 IEEE 5th International forum on Research and Technology for Society and Industry (RTSI), September 2019
Embedding Recurrent Neural Networks in Wearable Systems for Real-Time Fall Detection. E. Torti, A. Fontanella, M. Musci, N. Blago, D. Pau, F. Leporati, M. Piastra, Microprocessors and microsystems, September 2019
Intelligent Recognition of TCP Intrusions for Embedded Micro-controllers, Remi Varenne, Jean Michel Delorme, Emanuele Plebani, Danilo Pau, Valeria Tomaselli, International Conference on Image Analysis and Processing 2019, September 2, 2019
A New Scalable Architecture to Accelerate Deep Convolutional Neural Networks for Low Power IoT Applications Embedded World 2018 – Speeches
Intelligent Embedded and Real-Time ANN-based Motor Control for Multi-Rotor Unmanned Aircraft Systems, George Michael, Nectarios Efstathiou, Kyriacos Mantis, Theocharis Theocharides, Danilo Pau, Proceedings of 25th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC) Abu Dhabi, UAE October 23 - 25, 2017
Embedded Real-Time Fall Detection with Deep Learning on Wearable Devices; Euromicro DSD/SEAA 2018, August 29 – 31, 2018, Prague | Czech Republic
Automated generation of Single Shot Detector C library from a high level Deep learning framework, 4th International Forum on Research and Technologies for Society and Industry; Palermo, Italy, September 10-13 2018
Intelligent Cyber-Physical Systems for Industry 4.0, First IEEE International Conference on Artificial Intelligence for Industries, Sep 26, 2018 - Sep 28, 2018, Laguna Hills, CA
STM32Cube.AI: AI productivity boosted on STM32 MCU, D Pau, M Durnerin, V D’Alto, M Castro, tinyML Summit. Advances in ultra-low power Machine Learning technologies and applications March 20-21, 2019 Sunnyvale, California