ST Microelectronics has integrated a set of free software tools to help users jumpstart the design and deployment of connected, autonomous things embedding artificial intelligence (AI) locally.

The ST Edge AI Suite should simplify the development of AI products exploiting the Swiss semiconductor company’s general-purpose and automotive microcontrollers and microprocessors, smart sensors, and related tools for embedded AI.

It will expand and integrate multiple software tools, and evaluation and development kits introduced over the past ten years, while leveraging the existing AI ecosystem of machine-learning frameworks and partners such as Nvidia and AWS.

“We are moving towards a world with tens of billions of connected, autonomous things bringing value and convenience to users throughout all aspects of consumer life and enterprise productivity,” said Jean-Marc Chery, STM CEO. “To achieve this, AI algorithms will need to be run both in the cloud and on-device, at the edge across a broad range of devices: smartphones and connected personal devices, smart home and building control systems, industrial machines, cars and many more. STM products are already at the core of all those devices, but it is their combination with the industry-leading, free software suite we are announcing that will make the difference. This combination will enable the transition to a more intelligent edge, empowering customers of any size to deploy edge AI more easily and build their vision of this connected future leveraging STM’s hardware.”

The aim is to empower embedded developers who want to create optimised machine-learning models, data scientists who want to run their model on an embedded device, and product designers and creators who want to redefine products.

With free access, STM will enable users large and small, pooling resources and knowledge into a community-driven approach. The suite will further enable this transformation by federating the tools and their users around a broader edge AI community.

The first release of the ST Edge AI suite will be in the first half of 2024.

Edge AI is a crucial technology for businesses to transform their products by bringing intelligence and decision-making capabilities closer to the data source. This offers benefits in terms of speed, power consumption, privacy, security and cost-efficiency.

A washing-machine maker is adopting this and its product should be on the market next year. A machine-learning algorithm is creating a virtual sensor approach, estimating the weight of the clothes to be washed based on the motor current measurement. Another machine-learning algorithm is collecting data from a six-axis motion sensor to enable drum collision avoidance by calculating if the drum will touch the outer shell of the washing machine. Based on the algorithm input, a programme drives the motor using exactly the current needed and adjusts the water and detergent used to save between 15 and 40% energy and water for a washing cycle. Both algorithms have been developed with NanoEdge AI and are running on an STM32G0 MCU together with an STM six-axis motion sensor.

The Hewlett-Packard engineering team worked closely with STM to develop and train AI models that recognise different user activities based on device and user motion. Multiple use cases were addressed, including scenarios where a laptop is placed on a table, on the user’s lap, or carried inside a bag and taken out. This helped create an AI model specific to HP devices that was put on a smart six-axis motion sensor. This sensor is running an edge AI algorithm at 34µA. This allows HP computers to detect changes and respond accordingly, and, most importantly, conserve the battery power for more critical tasks.

STM is working with the HPE group to optimise the operation and maintenance of motors in electric vehicles. The challenge here was to monitor the internal temperature of the rotor of an electric motor in actual use, so power output could be optimised for more efficient and safer operation. This is something that could be done in a lab with the rotor exposed but is not possible in an actual running motor in a vehicle. The answer was to train a model and build a virtual temperature sensor with edge AI to estimate the internal rotor temperature from the external temperature measurement. The algorithm runs on a Stellar automotive MCU that controls the motor. The same MCU also runs an AI algorithm that detects potential anomalies through the analysis of vibrations.

This approach can be used with other critical components, such as EV batteries, where the internal temperature of the battery is not practical to measure but an edge AI model can simulate it from an external measurement.

STM’s strategy on AI relies on the availability of an integrated set of tools, some already available, technical and educational examples, and a unified optimiser of embedded AI called ST Edge AI Core Technology. The ST Edge AI Suite addresses the needs and requirements of different profiles, such as data scientists, embedded software developers and hardware engineers. The suite is easy to use, with an intuitive interface, available in desktop, CLI, web and API versions.

The ST Edge AI Suite works across multiple STM hardware platforms such as STM32 general-purpose MCUs, including the already announced portfolio with AI hardware acceleration.

The NanoEdge AI Studio AutoML tool becomes free for STM32, and is now available for all Arm Cortex-M based MCUs.

Further information can be found at www.st.com.