Artificial intelligence
at the edge

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.

STM32 edge AI solutions open the door to many new application possibilities. We have developed the cutting-edge ST Neural-ART Accelerator, a proprietary neural processing unit (NPU) that delivers exceptional efficiency in handling AI tasks. We also offer user-friendly online tools and software, allowing embedded developers to create, evaluate, and deploy machine learning algorithms on STM32 microcontrollers and microprocessors in a fast and cost-effective way.

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.

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.