Ultimate Guide To Machine Learning For Embedded Systems

How resource constrained systems use machine learning.


Machine learning is a subfield of artificial intelligence which gives computers an ability to learn from data in an iterative manner using different techniques. Our aim here being to learn and predict from data. This is a big diversion from other fields which poses the limitation of programming instructions instead of learning from them. Machine learning in embedded systems specifically target embedded systems to gather data, learn and predict for them. These systems typically consist of low memory, low Ram and minimal resources compared to our traditional computers.

So now you know a little more about what we mean by “machine learning for embedded systems”, but maybe
you’re still unsure about where or how to start? That’s why we’ve created the ultimate guide to machine learning for embedded systems. Over the last few years, as sensor and MCU prices plummeted and shipped volumes have gone thru the roof, more and more companies have tried to take advantage by adding sensor-driven embedded AI to their products.

Automotive is leading the trend – the average non-autonomous vehicle now has 100 sensors, sending data to 30-50 microcontrollers that run about 1m lines of code and generate 1TB of data per car per day.

Click here to read more.

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