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ST announces Edge AI Suite for Arm Cortex-M microcontrollers, sensors, and so on
ST has just formally announced the Edge AI Suite, a development tool set for its line of STM32 microcontrollers based on the Arm Cortex-M processor core, Arm-based Stellar automotive microcontrollers, and associated smart sensors.
www.eejournal.com/, Dec. 13, 2023 –
The Edge AI Studio is free to use when generating code for ST's microcontrollers. ST's NanoEdge AI Studio, one of the tools within the Edge AI Suite, can be licensed for use with Cortex-M microcontrollers offered by other semiconductor vendors. The Edge AI Suite and its associated libraries are not designed to implement large language models (LLMs) or for replicating cloud-based chatbots like ChatGPT inside of an air fryer, a toaster oven, or a washing machine. Instead, these tools are designed for developing edge and embedded applications where machine learning (ML) tasks can significantly improve the performance or feature set of an end product.
If you're like me, you might have some difficulty in envisioning just what such applications might be. In a December presentation where the company rolled out its new tool suite, ST provided examples that I found very helpful in visualizing these applications. The first example was a microcontroller-based washing machine. The microcontroller is already incorporated into the washing machine's design for motor control, with algorithms based on the STM32 motor-control SDK. Using ML techniques with no additional hardware, the microcontroller can determine the mass of the clothes placed in the washing machine by spinning the washing drum and using an ML algorithm to convert the motor's feedback voltage and current into a clothing mass estimate.
Using this technique, the microcontroller can determine the weight of the clothes within 100 grams, which is three times better than existing techniques. The mass of the clothes establishes the precise amounts of energy, water, and detergent needed to clean the clothes thoroughly, again using ML model calculations. The result is clean clothes with lower energy costs, less water consumption, and less detergent pollution in the resulting wastewater. According to ST, the addition of ML modeling reduces the washing machine's energy and water consumption by as much as 40%. Motor current consumption during the wash cycle can also be used for predictive maintenance using another ML model.
Yet another ML model takes streamed data from an ST 6-axis motion sensor in the washing machine to detect the position of the rotating washing drum. That information is used for collision avoidance, which prevents the spinning drum from hitting the stationary parts of the washing machine. All the ML models and software enhancements fit into the existing on-chip memory in the washing machine's STM32G0 microcontroller, so there's no incremental hardware cost for adding most of the ML models to this application. The one incremental hardware cost is the addition of the 6-axis sensor.
In addition, these ML tasks need no cloud connectivity. The washing machine can operate independently from the Internet, although connected appliances are a growing category as appliance manufacturers seek to bind customers ever more closely to their appliance brands to sell them accessories, consumables, maintenance, and other services. My relatively new Samsung Smart TV needs to call back to the Samsung mothership when it boots up, to see what features I'm entitled to use. Unfortunately, if it cannot contact Samsung, I get the equivalent of the Blue Screen of Death, which is a reminder that any connected appliance needs a fallback operating mode when it loses connection to the Internet. ST mentioned that a "major home appliance maker" will be introducing a product based on this ML technology some time in 2024.
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