What is the state of machine learning at the edge today? What tools can help engineers collect data and run inferences? Where can you find ST MEMS, and how can they make a difference in real-world products? This piece is the second part of our series on the upcoming STM32 Roadshow. For the 14th year in a row, we are reaching out to our community. We will hold demos, show products, and have engineers ready to answer questions. The first part of our STM32 Roadshow Series focused on cloud connectivity as we talked about a new smart doorbell demo. We also featured industrial and security applications. Today, we will explore artificial intelligence and computing as well as sensing.
STM32 Roadshow: Artificial Intelligence and Computing
Qeexo and STM32Cube.AI
The range of solutions enabling machine learning at the edge is also increasing, and the STM32Roadshow highlights the central role of STM32 MCUs. For example, we will show a demo of Qeexo’s AutoML. It uses a SensorTile to capture vibrations and sounds to detect if a fan is broken or blocked. It is a classic example of a predictive maintenance application that can vastly transform a factory’s operation with minimal investments. Qeexo is a member of the ST Partner Program.
There will also be numerous ST demos that leverage our machine learning solutions. Some of them are already popular, such as the STM32H747I-DISCO that uses machine learning to recognize dishes and drinks. It was a show favorite at the Technology Tour in Toronto and remains popular amongst our attendees. Our engineers will also demonstrate a system capable of reading a digital meter. This particular presentation uses an STM32WL, our first MCU, with an embedded LoRa transceiver.
Similarly, the STM32MP1 will run on a new AI demo offering multiple object detection. We rewrote the code in C to optimize it, and it will be the first time we show it in Asia. Moreover, we will showcase FP-AI-NANOEDG1, a Function Pack that allows developers to quickly test a Machine Learning library from Cartesiam on an STM32L5.
OpenMV
The STM32 Roadshow will be a great place to experience the OpenMV Cam H7 Plus. The product relies on an STM32H7 microcontroller to capture videos using a five-megapixel camera module on top of the PCB. Additionally, the platform works using MicroPython to make it easier to program. It thus puts a robust system in the hands of engineers and enthusiasts wishing to experiment with embedded systems quickly. Users can even download the OpenMV IDE and run example applications that will show some of the system’s capabilities.
The event will also demonstrate to attendees that they can go much further than the typical demos. For instance, Edge Impulse has a tutorial showing how to write a machine learning application with the OpenMV Cam H7 Plus. The ST Partner Program member facilitates the creation of neural networks that can then run inference operations on ST’s MCUs. In this instance, developers use the OpenMV PCB and IDE to collect data. They then send it to Edge Impulse for processing. Finally, users can export a neural network as an OpenMV library. This system is also impressive because as engineers transition to an industrial setting, it is possible to use Edge Impulse to get a neural network that will work with STM32Cube.AI. This software solution converts neural networks into optimized code for STM32 to vastly facilitate machine learning at the edge.
STM32 Roadshow: Sensing and Innovation
SensorTile.box and the Crying Baby Detector
The SensorTile.box will be another highlight of the STM32 Roadshow. Our most powerful sensor box with multiple user modes will be at the center of a few demos. Users will be able to interact with built-in demo applications. The STEVAL-MKSBOX1V1 (the reference of the SensorTile.box) with iOS and Android applications to quickly showcase some of its capabilities. For instance, ST provides a baby crying detector. The application first uses an algorithm that employs a Fast Fourier Transform to process the signal. It then runs the data through a neural network on the host STM32. Thanks in part to STM32Cube.AI, developers can use a regular MCU to distinguish between ambient noise and a child’s cries. This demo is also highly symbolic because it exemplifies how our sensors, MCUs, and more work to create unique and wholesome solutions.
OPPO Smartwatch and Edifier Dreampods
The STM32 Roadshow will also be an opportunity to check out significant design wins physically. For instance, we will showcase an OPPO smartwatch that includes our LPS27HHW waterproof barometer. The component can measure how deep a user is swimming or how high that person is climbing. The OPPO watch also includes the LSM6DSOW. It uses an ultra-low power accelerometer and a gyroscope to detect human activities while reducing the overall power consumption to optimize battery usage.
Similarly, we will also showcase the Edifier Dreampods. It is fascinating to learn how these wireless earphones use a LIS25BA to detect vibrations crawling from the inner ear to the facial bones. Such a system ensures the device can distinguish between the audio and ambient noise. The Dreampods also use the LIS2DH12 accelerometer to enable users to tap on the earphones to play or pause music and operate other controls, such as picking up a call or hanging up. Both the Dreampods and the OPPO smartwatch are available on the Chinese market.
We will publish pictures of the STM32 Roadshow at a later date.
Cities That Will Host the STM32 Roadshow
- Shenzhen/Hangzhou (13 Sep)
- Zhengzhou/Changsha (15 Sep)
- Nanjing/Zhuhai (17 Sep)
- Xiamen/Chongqing (19 Sep)
- Guangzhou/Beijing (21 Sep)
- Xi’an/Qingdao (23 Sep)
- Shenyang/Shanghai (25 Sep)
- Register for the STM32 Roadshow in China
