AI is used today in an ever-wider range of applications. It impacts the services we use as well as the devices and machines we interact with every day. Much of this AI relies on cloud computing, using powerful remote data centers, which process the data collected by local devices.
Edge AI deploys AI algorithms and models directly on devices such as Internet of Things (IoT) devices and embedded industrial and automotive systems. This approach enables real-time processing and analysis of data at the source. It paves the way for genuinely autonomous intelligent devices, capable of rapidly deciding and acting in an adaptive way.
Deploying AI at the edge offers several advantages over the cloud approach. It provides speed and ultralow latency, much lower data transmission loads, and significantly improved security. It significantly reduces power thanks to inference algorithms running at milliwatts, or even microwatts, on edge devices vs watts in the cloud. And it preserves privacy.
Edge AI opens many new possibilities for device and service creators across all markets enabling new applications at a fraction of the cost of using the cloud.
Whether you are a specialist or just starting to use edge AI, ST has solutions for you.
We invest in research, innovation, and development activities to create what our customers need to take advantage of the power of edge AI. We also actively participate in the tinyML community to further improve machine learning efficiency in small IoT devices.
Our portfolio of products and design tools allow embedded developers to quickly deploy AI on ST microcontrollers, microprocessors, and smart sensors, making AI more efficient and sustainable.
And it's just the beginning. Our latest technology breakthroughs are a game changer for edge AI. Find out how.
STM32 edge AI solutions make devices smarter and more energy efficient, improving the user experience and opening the door to many new application possibilities. We offer user-friendly online tools and software that help embedded developers create, evaluate, and deploy machine learning algorithms on STM32 microcontrollers and microprocessors in a fast and cost-effective way.Learn more
ST smart sensors embed a machine learning core or an advanced specialized digital signal processor (DSP) for edge AI, enabling context awareness in many applications, from industrial equipment to IoT devices. This allows sensors to process information and to share only meaningful data with the microcontroller. These smart sensors reduce power consumption at system level, further enhancing efficiency.Learn more
Engineers can enhance safety, efficiency, and the overall driving experience, by using our SPC5Studio.AI to convert, analyze, and deploy automotive neural network models on SPC58 microcontrollers. The edge AI plugin tool for the latest Stellar E microcontrollers is available upon request.Learn more
See our edge AI solutions in action
STM32 edge AI solutions
IRMA from Oxytronic, made with
NanoEdge AI Studio
STM32Cube.AI Developer Cloud: 4 steps to easily test and embed your neural network model
Discover ST's MEMS sensor with an intelligent core
How smart sensors make industrial applications more reactive and power efficient
Leveraging edge AI on the LSM6DSV16BX sensor for TWS in wireless earbuds
ISPU self-learning for personalized fitness training
Road state monitoring using edge AI
|Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks
|Ferheen Ayaz, Idris Zakariyya, José Cano, Sye Loong Keoh, Jeremy Singer, Danilo Pau, Mounia Kharbouche-Harrari
|Ultra-Tiny Neural Network for Compensation of Post-soldering Thermal Drift in MEMS Pressure Sensors
|Gian Domenico Licciardo, Paola Vitolo, Stefano Bosco, Santo Pennino, Danilo Pau, Massimo Pesaturo, Luigi Di Benedetto, Rosalba Liguori
|SRAM-Based All-Digital Up to 4b In-Memory Computing Multi-Tiled NN Accelerator in FD-SOI 18nm for Deep-Learning Edge Applications
|G. Desoli et al.
|End to End Optimized Tiny Learning for Repositionable Walls in Maze Topologies
|Danilo Pau, Stefano Colella and Claudio Marchisio
|TinyRCE: Forward Learning Under Tiny Constraints
|Danilo Pau, Prem Kumar Ambrose
|tinyML® Research Symposium
|A 0.8 mW TinyML-Based PDM-to-PCM Conversion for In-Sensor KWS Applications
|Paola Vitolo, Rosalba Liguori, Luigi Di Benedetto, Alfredo Rubino, Danilo Pau, Gian Domenico
|Licciardo Conference paper