Practical applications for AI and ML in embedded systems

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Embedded development is often driven by the need to deploy highly optimised and efficient systems. AI is positioned to disrupt businesses either by enabling new approaches to solving complex problems or threatening the status quo for whole business sectors or types of jobs.

electronicspecifier.com, Jan. 10, 2020 – 

Whether you understand what the excitement is all about and how it will be applied to your market, or you struggle to understand how you might take advantage of the technology, having some basic understanding of artificial intelligence and its potential applications has to be part of your strategic planning process.

Despite the hype, it is sobering to remember that artificial intelligence is not a magic trick that can do anything. It's a tool with which a magician can do a few tricks. One area that is gaining interest is how artificial intelligence may be applied to embedded systems, with a focus on how to plan for deployment in these more constrained environments.

Definitions and Basic Principles

To be sure we are all on the same page, let's start with some background about the different technologies and their compute requirements.

AI is a computer science discipline looking at how computers can be used to mimic human intelligence. AI has existed since the dawn of computing in the 20th Century, when pioneers such as Alan Turing foresaw the possibility of computers solving problems in similar ways in which humans might do so.

Classical computer programming solves problems by encoding algorithms explicitly in code, guiding computers to execute logic to process data, and compute an output. In contrast, Machine Learning (ML) is an AI approach that seeks to find patterns in data, effectively learning based on the data. There are many ways in which this can be implemented, including pre-labeling data (or not), reinforcement learning to guide algorithm development, extracting features through statistical analysis (or some other means), and then classifying input data against this trained data set to determine an output with a stated degree of confidence.

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