Quickly and efficiently implement machine learning at the network edge
07 February 2019
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Machine learning is migrating from the cloud to the network edge for real-time processing, lower latency, improved security, more efficient use of available bandwidth, and lower overall power consumption. As a result, developers of resource limited Internet of things (IoT) devices at these edge nodes need to figure out how to efficiently add this new level of intelligence.
Using machine learning at the edge and on a microcontroller-based system provides several new opportunities for developers to revolutionise the way that they design systems. There are several different architectures and techniques that developers can use to add intelligence to their edge nodes.
In this article from Digi-Key Electronics, you become more familiar with those architectures, along with some of the technologies that can be used to accelerate the process.
Read the full article here.
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