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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>VerilogEval: Evaluating Large Language Models for Verilog Code Generation · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2309.07544/</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/2309.07544/feed.xml" rel="self" type="application/rss+xml" /><item><title>VerilogEval: Evaluating Large Language Models for Verilog Code Generation — mentioned by pbazarnik</title><link>https://news.ycombinator.com/item?id=38187373</link><guid isPermaLink="false">urn:hackerpapers:mention:2309.07544:38187373</guid><pubDate>Wed, 08 Nov 2023 06:28:08 GMT</pubDate><description>&lt;p&gt;It looks like availability of good quality training sets will be a stumbling block for LLM use in Verilog chip design since pretraining with other programming language corpus is not transferable. (see below for quote from Nvidia paper) A lot of high quality Verilog is locked in licensed, close source IP blocks covered by NDAs. HDLBits problems are a toy level complexity circuits suitable for Verilog 101 course material. I would set a benchmark for serious HDL design LLM at reaching ability to implement AXI bus components with specified by user functionality, e.g. AXI4 Slave (address, data…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2309.07544/"&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>
