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	<title>GPU memory &#8211; BIOENGINEER.ORG</title>
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		<title>Attention pooling trims batch-one GPU memory by 62% in transformer hashing</title>
		<link>https://bioengineer.org/attention-pooling-trims-batch-one-gpu-memory-by-62-in-transformer-hashing/</link>
		
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		<pubDate>Mon, 26 Oct 2026 22:00:51 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[attention pooling]]></category>
		<category><![CDATA[Attention-enhanced]]></category>
		<category><![CDATA[binary codes]]></category>
		<category><![CDATA[deep hashing]]></category>
		<category><![CDATA[Dense re-ranking]]></category>
		<category><![CDATA[GPU memory]]></category>
		<category><![CDATA[image retrieval]]></category>
		<category><![CDATA[Inference efficiency]]></category>
		<category><![CDATA[MS-COCO]]></category>
		<category><![CDATA[NUS-WIDE]]></category>
		<category><![CDATA[vision]]></category>
		<category><![CDATA[Vision Transformer hashing]]></category>
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					<description><![CDATA[In a controlled comparison on one RTX 4090, attention pooling reduced batch-one GPU memory use by 62.0% while maintaining comparable retrieval accuracy on MS-COCO and NUS-WIDE.]]></description>
		
		
		
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