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STM32全国研讨会:且看Python 和OpenMV如何在 STM32 MCU上运行AI

2020-09-14 15:07 来源:意法半导体 编辑:电源网

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.

如今网络边缘侧的机器学习现状如何?哪些工具可以帮助工程师收集数据并执行推断运算?在哪里可以找到ST MEMS,它们对现实生活中的产品有哪些影响?本文是我们即将举行的STM32全国研讨会系列的第二篇专题文章。在第14届STM32全国研讨会上,我们将通过应用演示、产品展示以及工程师与观众互动回答问题的方式,来与蝶粉社区近距离交流。在STM32全国研讨会专题系列报道第一部分我们着重介绍了云连接方面的用例,如一款新的智能门铃功能演示,还介绍了工业和数据安全相关应用。 今天,我们将重点探讨人工智能、计算以及感知技术。

Artificial Intelligence and Computing

人工智能与计算

Qeexo 和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.

当今边缘机器学习解决方案的种类越来越多,本届STM32全国研讨会将聚焦讨论STM32 MCU在这类应用中的核心角色。我们将演示Qeexo’s AutoML工业自动化机器学习解决方案(Qeexo是ST合作伙伴计划成员)。该系统使用SensorTile捕获振动和噪声,检测风扇是否损坏或阻塞,这是一个经典的,以最少的投资来最大化提高工厂运营效率的预测性维护应用示例。

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.

全国研讨会上还有很多ST的机器学习应用演示,其中一些已经很有人气,例如,使用机器学习识别食品饮料的STM32H747I-DISCO。它在Technology Tour in Toronto(多伦多科技展)上广受关注,在本届全国研讨会参观者中也仍享有很高的人气。我们的工程师还将演示一个智能电表抄表系统,这个特别的演示使用的是STM32WL——我们的第一款带有嵌入式LoRa收发器的MCU。

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.

同样,STM32MP1将出现在一个新的AI多物体检测演示板上。我们重写并优化了C语言代码,这个解决方案是首次在亚洲演示。此外,ST还将展示一个使开发人员可以在STM32L5上快速测试Cartesiam机器学习库的FP-AI-NANOEDG1功能包。

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.

本届STM32全国研讨会将是观众体验OpenMV Cam H7 Plus的绝佳机会。该产品依靠STM32H7微控制器和PCB板载500万像素摄像模块拍摄视频。此外,该平台还可以支持MicroPython语言,使编程变得更轻松,它为那些希望快速测试嵌入式系统AI的工程师和发烧友提供了一个稳健的系统。用户甚至可以下载OpenMV IDE开发环境,运行系统功能演示应用程序,查看某些系统功能。

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.

观众还将在本届研讨会上了解到比一般demo演示更深层次的东西。例如,Edge Impulse(ST合作伙伴计划成员之一)有一个如何使用OpenMV Cam H7 Plus编写机器学习应用程序的教程,让开发在ST MCU上执行推断运算的神经网络变得更容易。在这个示例中,开发人员可以使用OpenMV PCB和IDE收集数据,然后,发送到Edge Impulse进行数据处理,最后,可以导出神经网络的OpenMV库。该系统令人印象深刻。随着工程师开始关注工业环境,使用Edge Impulse就可以获得一个支持STM32Cube.AI的神经网络。该软件解决方案将神经网络转换为可在STM32上运行的代码,从而极大地降低了边缘机器学习的开发难度。

Sensing and Innovation

感知与创新


SensorTile.box and the Crying Baby Detector

SensorTile.box和宝宝哭声检测器

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.

SensorTile.box将是STM32全国研讨会的另一个亮点。我们最强的多用户模式传感器模组SensorTile.box将是几场演示活动的核心角色。用户将能够与内置的演示应用程序互动,装有iOS和Android应用程序的STEVAL-MKSBOX1V1(Sensor-Tile.box的型号)可快速展示模组的部分功能,例如,ST提供的宝宝哭声检测器。该应用先是运行一个算法,采用快速傅立叶变换方法处理信号;然后,通过主控制器STM32上的神经网络运行数据。开发人员可以使用常规MCU辨别环境噪声和孩子的哭声,其中,STM32Cube.AI功不可没。该演示还具有高度的示范意义,因为它是一个展示我们的传感器、MCU等芯片如何协同工作,创建独特而有益的解决方案的范例。

OPPO Smartwatch and Edifier Dreampods

OPPO智能手表和漫步者耳机

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 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, which uses finite state machines to detect human activities while reducing the overall power consumption. The system can thus detect if a user is running or cycling while consuming very little to save its battery.

本届STM32全国研讨会的另一个看点是,观众将有机会看到几个重要的ST设计采纳用例。举例来说,我们将展示一个OPPO智能手表,这款手表内置我们的LPS27HHW气压计传感器,可以测量用户游泳水深或攀爬高度。OPPO手表还集成了LSM6DSOW惯性测量单元,它使用有限状态机检测人类活动,同时能够降低系统总体功耗。因此,该系统可以检测用户是在跑步还是骑车,而且几乎不耗电,十分节省电池电量。

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.

同样,我们还将展示漫步者的Dreampods耳机。了解这些无线耳机如何使用LIS25BA检测从内耳传向面部骨骼的振动对开发者抑或耳机发烧友而言无疑是一件非常有趣的事情。该系统确保设备可以区分音频和环境噪声。 Dreampods还集成了LIS2DH12加速度计,用户只要敲击耳机就可以播放或暂停音乐,还可以进行其他控制操作,例如接听电话或挂断电话。现今Dreampods和OPPO智能手表都能在中国市场买到。

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