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		<title>Tiny AI Chip Upgrade Slashes X-ray Imaging Bottleneck at World&#8217;s Most Powerful Lasers</title>
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				<category><![CDATA[Technology]]></category>
		<category><![CDATA[Edge Computing]]></category>
		<category><![CDATA[FPGA]]></category>
		<category><![CDATA[GPU comparison]]></category>
		<category><![CDATA[LCLS]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[neural network compression]]></category>
		<category><![CDATA[real-time inference]]></category>
		<category><![CDATA[scientific computing]]></category>
		<category><![CDATA[SLAC Neural Network Library]]></category>
		<category><![CDATA[SpeckleNN]]></category>
		<category><![CDATA[X-ray single-particle imaging]]></category>
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					<description><![CDATA[Researchers at SLAC National Accelerator Laboratory have compressed a neural network by 98.8% and deployed it on an FPGA chip, classifying X-ray speckle patterns 8.9 times faster and using 7.8 times less power than a leading GPU.]]></description>
		
		
		
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