AI (Artificial Intelligence) originated from the summer seminar held by Dartmouth College in 1956. At the conference, the term “artificial intelligence” was formally proposed for the first time. Technological breakthroughs in computing power have promoted the development of artificial intelligence one after another. In recent years, with the increasing availability of big data, the third wave of artificial intelligence development has arrived. In 2015, the artificial intelligence algorithm based on deep learning surpassed humans for the first time in the image recognition accuracy of the ImageNet competition, and artificial intelligence has made great strides on the road of development. With breakthroughs in computer vision technology research, deep learning has achieved great success in different research fields such as speech recognition and natural language processing. Now, artificial intelligence has shown great potential in all aspects of life.
Combined with the development stage of artificial intelligence technology, some main concepts are roughly explained as follows.
AI: All technologies that allow computer brains to simulate human behavior.
Machine learning: a subset of artificial intelligence (AI). Algorithms and methods that are continuously improved by learning from data.
Deep learning: a subset of machine learning (ML). By using a multi-layer structure that simulates the neural network of the human brain, a learning algorithm that obtains valuable information from a large amount of data.
2. The new force of artificial intelligence, STMicroelectronics Deep Edge AI came into being
At present, artificial intelligence technology is mainly used in cloud scenarios because of the demand for computing power. Due to the limitation of data transmission delay and other factors, cloud-based solutions may not be able to meet the needs of some users for data security, system responsiveness, privacy, and local node power consumption. In a centralized artificial intelligence solution, embedded devices (smart speakers, wearable devices, etc.) usually rely on cloud servers to achieve artificial intelligence capabilities, while in the Deep Edge AI solution, the embedded device itself can run artificial intelligence locally Algorithms to realize real-time environment perception, human-computer interaction, decision-making control and other functions.
Moving the reasoning process to deep edge computing will bring some advantages, such as system responsiveness, better user information privacy protection (not all data needs to be transmitted to the cloud through multiple systems), and lower connection costs and power consumption.
According to ABI’s research results, global shipments of Deep Edge AI devices will reach 2.5 billion units by 2030. STMicroelectronics has noticed that there are more and more communities and ecosystems surrounding Deep Edge AI technology, focusing on independent, low-power and cost-effective embedded solutions. As the main promoter of this trend, STMicroelectronics has invested a lot of resources in AI, aiming to help developers in embedded systems based on microcontrollers/microprocessors (STM32 series) and sensors (MEMS, ToF…) Rapid deployment of AI applications. STMicroelectronics provides a set of AI tools for the STM32 series and MEMS sensors that integrate the machine learning core (MLC), which can speed up the development cycle and optimize the trained AI model (STM32Cube.AI).
As a general technology, artificial intelligence has made remarkable achievements in many fields. We believe that more and more smart terminal devices will have a more direct and positive impact on human life.
3. Rapid deployment of AI applications through STMicroelectronics’ ecosystem
STMicroelectronics provides an ecosystem of hardware and software to help quickly and easily develop a variety of Deep Edge AI algorithms for sensors and microcontrollers.
The machine learning in the MEMS sensor ecosystem helps designers use AI at the Edge to implement gestures, activity recognition, anomaly detection, etc. through a decision tree classifier running on a sensor embedded engine called the Machine Learning Core (MLC).
Therefore, IoT solution developers can deploy any of our sensors (embedded with machine learning core) in the rapid prototyping environment to quickly develop ultra-low-power applications using UNICO-GUI tools.
With the built-in low-power sensor design, advanced AI event detection, wake-up logic, and real-time edge computing functions, the MLC in the sensor greatly reduces the amount of system data transmission and reduces the burden of network processing.
If developers decide to develop a solution based on the core of machine learning in the sensor, they need a new set of methods to publish their applications.
If you want to create any machine learning algorithm, the starting point is the data and its definition of the class (used to describe the complex problem to be solved). You can follow five steps to create and run AI applications in the sensor. UNICO-GUI is a graphical user interface that can support all five steps including decision tree generation.
In order to facilitate developers to quickly deploy trained AI models to STM32, we have developed an easy-to-use and efficient tool-STM32Cube.AI (also known as X-CUBE-AI). X-CUBE-AI can analyze and convert the trained neural network into optimized C language code, and automatically test against STM32 targets. Of course, X-CUBE-AI is a very powerful tool, and more features will be introduced in subsequent articles.
In order to show how several different AI applications can run directly on STM32 and speed up the development, verification and deployment process of STM32 embedded developers, STMicroelectronics provides many AI applications as a reference.
Developers can perform secondary development based on these embedded AI application software packages to quickly implement the deployment of custom models.
More details will be introduced in subsequent articles.
AI development tools and embedded application software packages are summarized as follows
Embedded Software
Where there is STM32, there is Deep Edge AI.
All MCUs of STM32 support the deployment of AI models. For MCUs with low computing power, machine learning algorithms (ML) are supported. For MCUs with higher computing power, neural network models (DL) are also supported.
The list of evaluation boards that can run the application examples is summarized below.
Product Evaluation Tool
4. Want more details?
We will publish a series of articles detailing the results of STMicroelectronics’ efforts in the Deep Edge AI field.
You are welcome to explain what you want to know about STMicroelectronics AI in the comments, and we will present you with more exciting content.