<?xml-stylesheet type="text/xsl" href="https://devzone.nordicsemi.com/cfs-file/__key/system/syndication/rss.xsl" media="screen"?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:slash="http://purl.org/rss/1.0/modules/slash/" xmlns:wfw="http://wellformedweb.org/CommentAPI/"><channel><title>Powering ultra-low-energy edge AI with custom Neuton models</title><link>/nordic/nordic-blog/b/blog/posts/introducing-custom-neuton-models</link><description>Custom Neuton models enable tiny, low-power edge AI on any Nordic SoCs letting developers build efficient, data-driven ML for IoT without heavy frameworks.</description><dc:language>en-US</dc:language><generator>Telligent Community 13</generator><item><title>RE: Powering ultra-low-energy edge AI with custom Neuton models</title><link>https://devzone.nordicsemi.com/nordic/nordic-blog/b/blog/posts/introducing-custom-neuton-models</link><pubDate>Thu, 18 Jun 2026 04:23:00 GMT</pubDate><guid isPermaLink="false">137ad170-7792-4731-bb38-c0d22fbe4515:99d1db6c-41fb-43e9-9ccb-6194d7e6b496</guid><dc:creator>ImRizwan</dc:creator><slash:comments>0</slash:comments><description>&lt;p&gt;Really appreciate the detailed walkthrough here. The part about Neuton growing the network neuron by neuron instead of requiring manual architecture decisions is what stood out most &amp;mdash; that&amp;#39;s a genuine shift from how most embedded ML workflows operate today. The data collection guidelines were also more practical than most docs I&amp;#39;ve seen on this. Going to experiment with the Edge AI Lab on a wearable project we&amp;#39;re working on. Thanks for putting this together&lt;/p&gt;
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