<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>grandmczeb</author><text>Here&amp;#x27;s the bottom line for anyone who doesn&amp;#x27;t want to read the whole article.&lt;p&gt;&amp;gt; Using a commercially available 28-nanometer ASIC process technology, we have profiled (8, 1, 5, 5, 7) log ELMA as 0.96x the power of int8&amp;#x2F;32 multiply-add for a standalone processing element (PE).&lt;p&gt;&amp;gt; Extended to 16 bits this method uses 0.59x the power and 0.68x the area of IEEE 754 half-precision FMA&lt;p&gt;In other words, interesting but not earth shattering. Great to see people working in this area though!</text><parent_chain></parent_chain></comment><story><title>Making floating point math highly efficient for AI hardware</title><url>https://code.fb.com/ai-research/floating-point-math/</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>moflome</author><text>Not sure why this isn&amp;#x27;t getting more votes, but it&amp;#x27;s a good avenue of research and the authors should be commended. That said, this approach to optimizing floating point implementations has a lot of history at Imagination Technologies, ARM and similar low-power inferencing chipsets providers. I especially like the Synopsys ASIP Design [0] tool which leverages the open-source (although not yet IEEE ratified) LISA 2.0 Architecture Design Language [1] to iterate on these design issues.&lt;p&gt;Interesting times...&lt;p&gt;[0] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.synopsys.com&amp;#x2F;dw&amp;#x2F;ipdir.php?ds=asip-designer&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.synopsys.com&amp;#x2F;dw&amp;#x2F;ipdir.php?ds=asip-designer&lt;/a&gt;
[1] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;en.wikipedia.org&amp;#x2F;wiki&amp;#x2F;LISA_(Language_for_Instruction_Set_Architecture)&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;en.wikipedia.org&amp;#x2F;wiki&amp;#x2F;LISA_(Language_for_Instruction...&lt;/a&gt;</text><parent_chain></parent_chain></comment><story><title>Making floating point math highly efficient for AI hardware</title><url>https://code.fb.com/ai-research/floating-point-math/</url></story>