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	<title>Comments on: 16 Views of Hot Chips &#8217;17 &#124; EE Times</title>
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	<link>https://www.epanorama.net/blog/2017/08/25/16-views-of-hot-chips-17-ee-times/</link>
	<description>All about electronics and circuit design</description>
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		<title>By: Tomi Engdahl</title>
		<link>https://www.epanorama.net/blog/2017/08/25/16-views-of-hot-chips-17-ee-times/comment-page-1/#comment-1560289</link>
		<dc:creator><![CDATA[Tomi Engdahl]]></dc:creator>
		<pubDate>Fri, 25 Aug 2017 13:42:02 +0000</pubDate>
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		<description><![CDATA[Google Fellow: Neural Nets Need Optimized Hardware
http://www.eetimes.com/document.asp?doc_id=1332185

If you aren&#039;t currently considering how to use deep neural networks to solve your problems, you almost certainly should be, according to Jeff Dean, a Google senior fellow and leader of the deep learning artificial intelligence research project known as Google Brain.

In a keynote address at the Hot Chips conference here Tuesday (Aug. 22), Dean outlined how deep neural nets are dramatically reshaping computational devices and making significant strides in speech, vision, search, robotics and healthcare, among other areas. He said hardware systems optimized for performing a small handful of specific operations that make up the vast majority of machine learning models would create more powerful neural networks.]]></description>
		<content:encoded><![CDATA[<p>Google Fellow: Neural Nets Need Optimized Hardware<br />
<a href="http://www.eetimes.com/document.asp?doc_id=1332185" rel="nofollow">http://www.eetimes.com/document.asp?doc_id=1332185</a></p>
<p>If you aren&#8217;t currently considering how to use deep neural networks to solve your problems, you almost certainly should be, according to Jeff Dean, a Google senior fellow and leader of the deep learning artificial intelligence research project known as Google Brain.</p>
<p>In a keynote address at the Hot Chips conference here Tuesday (Aug. 22), Dean outlined how deep neural nets are dramatically reshaping computational devices and making significant strides in speech, vision, search, robotics and healthcare, among other areas. He said hardware systems optimized for performing a small handful of specific operations that make up the vast majority of machine learning models would create more powerful neural networks.</p>
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