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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Efficient Memory Management for Deep Neural Net Inference · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2001.03288/</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/2001.03288/feed.xml" rel="self" type="application/rss+xml" /><item><title>Efficient Memory Management for Deep Neural Net Inference — mentioned by almostgotcaught</title><link>https://news.ycombinator.com/item?id=42179023</link><guid isPermaLink="false">urn:hackerpapers:mention:2001.03288:42179023</guid><pubDate>Tue, 19 Nov 2024 00:50:36 GMT</pubDate><description>&lt;p&gt;there are a bunch of papers on DSA from the OR people http://adambuchsbaum.com/papers/dsa-stoc03.pdf https://link.springer.com/chapter/10.1007/978-3-540-27798-9_... https://users.cs.northwestern.edu/~pdinda/ics-s05/doc/dsa.pd... there are also some from ML people trying to allocate memory optimally for DNNs: https://arxiv.org/abs/1907.01989 https://arxiv.org/abs/1804.10001 https://arxiv.org/abs/2001.03288 they all boil down to a couple of greedy heuristics. the most recent &amp;quot;cool&amp;quot; paper was from a group at google https://dl.acm.org/doi/10.1145/3567955.3567961 basic idea is to use both ILP and…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2001.03288/"&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>
