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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Chip Placement with Deep Reinforcement Learning · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2004.10746/</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/2004.10746/feed.xml" rel="self" type="application/rss+xml" /><item><title>Chip Placement with Deep Reinforcement Learning — mentioned by ggm</title><link>https://news.ycombinator.com/item?id=36501873</link><guid isPermaLink="false">urn:hackerpapers:mention:2004.10746:36501873</guid><pubDate>Wed, 28 Jun 2023 03:23:09 GMT</pubDate><description>&lt;p&gt;This is a very harsh critique of googles approaches to peer-review publishing of ideas. Because they refused to publish sufficient details, it was impossible to reproduce results and when people dug deeper, it was performing WORSE than other mechanistic approaches to placement, not better. The author takes Nature to task for lack of rigour accepting the publication. Well, I think it reflects badly on Google and Nature both. They chose Nature presuambly for the prestige. On the other hand, it might this guys nose was out of joint because some co-published work was declined. It remains unclear…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2004.10746/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Chip Placement with Deep Reinforcement Learning — mentioned by ArtWomb</title><link>https://news.ycombinator.com/item?id=27040941</link><guid isPermaLink="false">urn:hackerpapers:mention:2004.10746:27040941</guid><pubDate>Tue, 04 May 2021 18:27:07 GMT</pubDate><description>&lt;p&gt;No question. It was video gaming from the period roughly spanning 1990-2010 that funded gpu innovation. But programmable compute shaders caught on very rapidly in science. And Nvidia was quick to not just recognize the new market, but bet the company that supercomputing would one day be gpu cluster based. Here&amp;#x27;s Jensen Huang talking to Stanford students about the birth of the Cg language (27:40&amp;quot; mark). The entire talk is gold. A text book case study of Moore&amp;#x27;s Law and the SV model of risk capital: https://www.youtube.com/watch?v=Xn1EsFe7snQ Ironically, IC Design itself is a strong candidate…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2004.10746/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Chip Placement with Deep Reinforcement Learning — HN submission</title><link>https://news.ycombinator.com/item?id=24889330</link><guid isPermaLink="false">urn:hackerpapers:submission:2004.10746:24889330</guid><pubDate>Sun, 25 Oct 2020 19:50:29 GMT</pubDate><description>&lt;p&gt;Chip Placement with Deep Reinforcement Learning&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;3 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2004.10746/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Chip Placement with Deep Reinforcement Learning — HN submission</title><link>https://news.ycombinator.com/item?id=23069629</link><guid isPermaLink="false">urn:hackerpapers:submission:2004.10746:23069629</guid><pubDate>Mon, 04 May 2020 16:02:35 GMT</pubDate><description>&lt;p&gt;Chip Placement with Deep Reinforcement Learning&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;2 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2004.10746/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Chip Placement with Deep Reinforcement Learning — HN submission</title><link>https://news.ycombinator.com/item?id=22958362</link><guid isPermaLink="false">urn:hackerpapers:submission:2004.10746:22958362</guid><pubDate>Thu, 23 Apr 2020 17:21:46 GMT</pubDate><description>&lt;p&gt;Chip Placement with Deep Reinforcement Learning&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;4 points; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2004.10746/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item><item><title>Chip Placement with Deep Reinforcement Learning — HN submission</title><link>https://news.ycombinator.com/item?id=22952870</link><guid isPermaLink="false">urn:hackerpapers:submission:2004.10746:22952870</guid><pubDate>Thu, 23 Apr 2020 04:42:35 GMT</pubDate><description>&lt;p&gt;Chip Placement with Deep Reinforcement Learning&lt;/p&gt;&lt;p&gt;&lt;/p&gt;&lt;p&gt;1 point; 0 discussion replies on HN. &lt;a href="https://hackerpaper.kaipfister.de/p/2004.10746/"&gt;Paper and source history&lt;/a&gt;. A submission is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
