<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hardware on Den's Hub: Technology Solutions, Guides and Best Practices</title><link>https://denshub.com/en/tags/hardware/</link><description>Recent content in Hardware on Den's Hub: Technology Solutions, Guides and Best Practices</description><generator>Hugo</generator><language>en</language><copyright>This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.</copyright><lastBuildDate>Sun, 08 May 2022 18:32:39 +0200</lastBuildDate><atom:link href="https://denshub.com/en/tags/hardware/index.xml" rel="self" type="application/rss+xml"/><item><title>New NVIDIA Jetson Xavier NX Super Module</title><link>https://denshub.com/en/nvidia-jetson-small-ai-computer/</link><pubDate>Sat, 16 May 2020 12:32:39 +0200</pubDate><guid>https://denshub.com/en/nvidia-jetson-small-ai-computer/</guid><description>&lt;p&gt;NVIDIA® Jetson Xavier™ NX brings supercomputer performance to the edge in a small form factor system-on-module (SOM). Up to 21 TOPS of accelerated computing delivers the horsepower to run modern neural networks in parallel and process data from multiple high-resolution sensors — a requirement for full AI systems.&lt;/p&gt;
&lt;h2 id="cloud-native" class="headerLink"&gt;&lt;a href="#cloud-native" class="header-mark" aria-label="Permalink to Cloud Native"&gt;&lt;/a&gt;Cloud Native
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&lt;p&gt;Jetson Xavier NX now features cloud-native support that lets developers build and deploy high-quality, software-defined features on embedded and edge devices. Pre-trained AI models from NVIDIA NGC and the NVIDIA Transfer Learning Toolkit give you a faster path to trained and optimized AI networks, while containerized deployment to Jetson devices allows flexible and seamless updates. Jetson Xavier NX accelerates the NVIDIA software stack with more than 10X the performance of its widely adopted predecessor, Jetson TX2.&lt;/p&gt;</description></item></channel></rss>