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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1807.07928/</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/1807.07928/feed.xml" rel="self" type="application/rss+xml" /><item><title>Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices — mentioned by Jun8</title><link>https://news.ycombinator.com/item?id=19840604</link><guid isPermaLink="false">urn:hackerpapers:mention:1807.07928:19840604</guid><pubDate>Mon, 06 May 2019 15:59:47 GMT</pubDate><description>&lt;p&gt;Chip was designed by Vivien Sze&amp;#x27;s Energy-Efficient Multimedia Systems Group at MIT (http://www.rle.mit.edu/eems/). Key takeaway: &amp;quot;Sze’s chip is called Eyeriss. Developed in collaboration with Joel Emer, a research scientist at Nvidia and a professor at MIT, it was tested alongside a number of standard processors to see how it handles a range of different deep-learning algorithms. By balancing efficiency with flexibility, the new chip achieves performance 10 or even 1,000 times more efficient than existing hardware does, according to a paper (https://arxiv.org/abs/1807.07928) posted online…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1807.07928/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.DC</category></item></channel></rss>
