<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>In-Pipeline Integration of Digital In-Memory-Computing into RISC-V Vector Architecture to Accelerate Deep Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2602.01827/</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/2602.01827/feed.xml" rel="self" type="application/rss+xml" /><item><title>In-Pipeline Integration of Digital In-Memory-Computing into RISC-V Vector Architecture to Accelerate Deep Learning — mentioned by tesnorindian</title><link>https://news.ycombinator.com/item?id=49489425</link><guid isPermaLink="false">urn:hackerpapers:mention:2602.01827:49489425</guid><pubDate>Sat, 29 Aug 2026 12:36:17 GMT</pubDate><description>&lt;p&gt;In memory compute has been in talks since LLMs took up. I remember few flocks were trying to get RISC V cores in the memory like these papers https://arxiv.org/abs/2602.01827&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2602.01827/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.AR</category></item></channel></rss>
