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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Deep Learning Models on CPUs: A Methodology for Efficient Training · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2206.10034/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2206.10034/feed.xml" rel="self" type="application/rss+xml" /><item><title>Deep Learning Models on CPUs: A Methodology for Efficient Training — mentioned by imiric</title><link>https://news.ycombinator.com/item?id=34399511</link><guid isPermaLink="false">urn:hackerpapers:mention:2206.10034:34399511</guid><pubDate>Mon, 16 Jan 2023 11:20:18 GMT</pubDate><description>&lt;p&gt;I&amp;#x27;m not deeply familiar with this space, but isn&amp;#x27;t training and inference on CPUs gaining some traction[1][2][3]? It&amp;#x27;s still surely orders of magnitude slower, but as CPUs get more powerful and ML frameworks get more efficient, making this cheaper and more accessible would be a major breakthrough. [1]: https://arxiv.org/abs/2206.10034 [2]: https://www.intel.com/content/www/us/en/developer/articles/t... [3]: https://www.usenix.org/conference/atc19/presentation/liu-yiz...&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2206.10034/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
