Artificial intelligence (AI) and machine learning (ML) are hot topics at the moment, and likely will remain in vogue for the next few years at least. In the embedded space, we tend to concern ourselves more with ML, as it’s the technology that lets the industrial systems morph themselves into data-generating platforms, able to adjust their characteristics on the fly based on intel received dynamically. That’s a mouthful, but basically, the machines learn about conditions and realign themselves to match those conditions.
To help developers in the process of building ML systems, the microcontroller vendors are making it easier than ever before by adding in the necessary hooks. For example, and as seen at the Embedded Technologies Conference, NXP’s Vice President of Software Engineering, Rob Oshana, will walk through the steps required to build an ML system for an embedded application. He’ll go through each step, the technologies available, how to choose the best technology, and how to integrate an embedded application to support machine learning. A case study is shown to demonstrate each step of the process, culminating in a real time embedded system machine learning application.
Using machine learning can add a level of intelligence to your embedded system that would otherwise be far too time consuming to implement with traditional methods. This could encompass neural networks to learn sensor and system behavior and identify when that behavior requires a human intervention.
To that end, Sai Yamanoor, an IoT applications engineer from Praxair, will discuss anomaly detection using machine learning at the conference. He will present the different toolsets that are available from the MCU vendors that can be used to implement machine learning algorithms on a microcontroller.
In many embedded/industrial applications, using ML in an MCU-based system can alter how the system works from a higher level. Nothing can be taken for granted. To address this issue, Intel’s Michael Clay will use vision as his design example and show just what capabilities are needed and what effects they have downstream.
While at the conference, you don’t want to miss Google’s take on machine learning, as explained by Hongyang Deng, a software engineer and technical lead manager of the machine learning team at Google. Hongyang will give an overview on the challenges and opportunities for running machine learning models on wearable and associated devices. The topics covered include use cases, constraints, model constructions and optimization, and tools to run the models with memory/MIPS restrictions.
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