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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1912.11951/</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/1912.11951/feed.xml" rel="self" type="application/rss+xml" /><item><title>EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation — mentioned by osaariki</title><link>https://news.ycombinator.com/item?id=27519026</link><guid isPermaLink="false">urn:hackerpapers:mention:1912.11951:27519026</guid><pubDate>Tue, 15 Jun 2021 17:57:20 GMT</pubDate><description>&lt;p&gt;I&amp;#x27;ve done work on low-latency FHE neural network inferencing [1] and we estimated the FLOPS to be approximately 4 times that of an Intel 8087 floating point coprocessor [2]. This was for a LeNet-5 network on the MNIST dataset with multicore evaluation on a workstation class machine. My view is that this is already fast enough to support use cases that really need the unique capabilities of FHE. Since this work we&amp;#x27;ve been focused on making FHE more usable with compilers and tooling [3]. Currently most FHE is being programmed like that Intel 8087 was: with the equivalent of assembly by directly…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1912.11951/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.CR</category></item></channel></rss>
