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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Low Latency Privacy Preserving Inference · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1812.10659/</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/1812.10659/feed.xml" rel="self" type="application/rss+xml" /><item><title>Low Latency Privacy Preserving Inference — mentioned by osaariki</title><link>https://news.ycombinator.com/item?id=21640218</link><guid isPermaLink="false">urn:hackerpapers:mention:1812.10659:21640218</guid><pubDate>Tue, 26 Nov 2019 17:09:32 GMT</pubDate><description>&lt;p&gt;You&amp;#x27;re right, CryptoNets used a data layout optimized for throughput with a batch size 4096. Since then we&amp;#x27;ve done a lot of work on low latency inference with our CHET compiler [1] and my colleagues with LoLa [2]. It all comes down to the data layouts you use. [1]: https://www.cs.utexas.edu/~roshan/CHET.pdf [2]: https://arxiv.org/pdf/1812.10659.pdf&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1812.10659/"&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>
