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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1805.08624/</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/1805.08624/feed.xml" rel="self" type="application/rss+xml" /><item><title>Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit — mentioned by edwintorok</title><link>https://news.ycombinator.com/item?id=20490798</link><guid isPermaLink="false">urn:hackerpapers:mention:1805.08624:20490798</guid><pubDate>Sun, 21 Jul 2019 11:06:03 GMT</pubDate><description>&lt;p&gt;Have you evaluated alternatives such as posits, Kulisch accumulation, and zfp? https://arxiv.org/pdf/1805.08624.pdf https://arxiv.org/pdf/1811.01721.pdf https://insidehpc.com/2018/05/universal-coding-reals-alterna... In particular the latter describes a generic framework that can be used to generate a lot of different number systems. Could hardware implement this, allowing us to compose and choose the number system by just setting some simple flags?&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1805.08624/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CV</category></item></channel></rss>
