September 22, 2023
blog
When it comes to AIoT, you need to understand terms and techniques such as edge computing, machine learning (ML), TinyML, anomaly detection, natural language processing, computer vision, and predictive maintenance.
Most of these technologies will be discussed in the following articles. Renesas AI Live Virtual Conference. But here are some things you should know. For example, edge computing unfortunately means different things to different people. In some circles, it refers to an “edge computer,” or a conduit from your local IoT system to the cloud. For others, it refers to the point at which the data is obtained. This is also called the end point.
In the context of this blog, let’s use the latter definition. In fact, there are many examples where AIoT systems do not need to be connected to the cloud, as they have the ability to handle all processing internally. In some cases, you may experience this problem with his MCUs with low power consumption, which are very different from what was available a few years ago. Even when using the cloud, connections can be made more efficient through “pre-processing” that can be performed at the edge. Another relatively new concept is that this AI work does not rely on special hardware blocks (such as TensorFlow processors) and takes full advantage of the DSP enhancements available on Arm M-core devices (M4 and above). That’s it.
Whether data is processed at the edge or in the cloud, developers are learning how to take advantage of today’s faster 5G and other low-latency networks. The deployment of 5G networks will enable faster and more reliable communication between IoT devices, which is essential for real-time AI processing.
Modern AI models, especially deep learning models, are optimized for deployment to resource-constrained IoT devices. Further simplified to machine learning algorithms, the software takes input directly from sensors at the edge and incorporates the data it needs to optimize operations. This is important for predictive maintenance applications. This application uses AIoT to predict when IoT devices and equipment need maintenance by analyzing sensor data for signs of wear and tear, reducing downtime and costs. This is also where TinyML can be designed.
By deploying lightweight (small) machine learning models, your code can be used much more efficiently with lower power and potentially cheaper MCUs. These models are clearly highly optimized for resource-constrained environments. Natural language processing works to enable devices to recognize voice commands and text-based interactions with devices.
Anomaly detection is a feature that is part of machine learning. As we know, IoT devices tend to generate large amounts of data, and AI-powered anomaly detection techniques can identify anomalous patterns and events. Not only does it help the machine run efficiently, but it can also be used for security purposes.
Computer vision is used in a variety of applications, from security applications to quality control. Using AI algorithms, the camera can handle object recognition, facial recognition, and even gesture control. You can discover everything from defective products rolling down the assembly line to people who shouldn’t be in a certain area.
As technology continues to evolve at a very fast pace, it is important for developers to keep up to date with the latest developments in AI and IoT in order to make informed decisions when implementing these technologies in their projects and applications. Staying on top of the situation is essential.
Richard Nass’ primary responsibilities include setting the direction for all aspects of OSM’s ECD portfolio, including digital, print and live events. Previously, Nass was the brand director of Design News. Previously, he led the Content Team and all Custom Properties and Events for UBM’s Medical Devices Group. Nass has been in the engineering OEM industry for over 30 years. Previously, he led content teams at EE Times, Embedded.com, and TechOnLine. Nass earned his BSEE degree from NJIT.
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