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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>AdderNet: Do We Really Need Multiplications in Deep Learning? · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1912.13200/</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/1912.13200/feed.xml" rel="self" type="application/rss+xml" /><item><title>AdderNet: Do We Really Need Multiplications in Deep Learning? — mentioned by fgfm</title><link>https://news.ycombinator.com/item?id=39541673</link><guid isPermaLink="false">urn:hackerpapers:mention:1912.13200:39541673</guid><pubDate>Wed, 28 Feb 2024 18:12:10 GMT</pubDate><description>&lt;p&gt;It&amp;#x27;s funny how discoveries in NLP &amp;amp; computer vision complement each other. The replacement of multiplication by additions made me think about the AdderNet paper (https://arxiv.org/abs/1912.13200), which concluded as you had to suffer almost no performance drop. Perhaps the accumulators in current hardware cannot leverage this to its full potential, but combined with such a strict quantization, this would open LLM to the wider ML community much earlier than expected (when consumer hardware allows you to train near SOTA LLMs from scratch on your machine).&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1912.13200/"&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>
