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  <created>1477513043</created>
  <changed>1477513324</changed>
  <title><![CDATA[GREEN Lab Team Wins IEEE S3S Best Student Paper Award]]></title>
  <body><![CDATA[<p>Mohammad Faisal Amir, Duckhwan Kim, and Jae Ha Kung received the Best Student Paper Award at the IEEE SOI-3D-Subthreshold Microelectronics Technology Unified Conference (IEEE S3S), held October 10-13, 2016 in San Francisco, California.</p>

<p>These Ph.D. students from the Georgia Tech School of Electrical and Computer Engineering (ECE) were honored for their paper entitled &ldquo;NeuroSensor: A 3D Image Sensor with Integrated Neural Accelerator.&rdquo; Their co-authors on the paper are their Ph.D. advisor and ECE Professor Saibal Mukhopadhyay, ECE Regents&rsquo; Professor Sudhakar Yalamanchili, and Denny Lie, Mukhopadhyay&rsquo;s former Ph.D. student who now works at Intel. Amir, Kim, and Kung are all current members of the Gigascale Reliable Energy Efficient Nanosystem (GREEN) Lab, which is led by Mukhopadhyay.</p>

<p>Neuro-inspired&nbsp;deep&nbsp;learning algorithms have emerged as a key&nbsp;enabler for performing complex tasks such as feature extraction, classification, and recognition on&nbsp;image/video data. Currently, such complex processing is mainly performed at the back-end&nbsp;machines. There is a&nbsp;growing need to push&nbsp;learning algorithms&nbsp;into the sensor front-end to&nbsp;facilitate&nbsp;fast decision making and reduce the&nbsp;demand on&nbsp;communication&nbsp;bandwidth. However, the size, weight, and power&nbsp;constraints limit the ability to&nbsp;perform complex processing&nbsp;in the&nbsp;sensor.</p>

<p>This paper presents an&nbsp;innovative system design&nbsp;concept&nbsp;to enable integration of&nbsp;deep learning architecture&nbsp;within a&nbsp;image&nbsp;sensor.&nbsp;The device is known as the NeuroSensor &ndash; a 3D stacked image sensor with an integrated neural network. The NeuroSensor performs on-chip neural computation for in-field decision making and classification, and thus reduces latency and energy consumption for data movement between the sensor and host.&nbsp;The&nbsp;design&nbsp;and analysis presented in the paper creates a new research&nbsp;direction on how to enable complex machine learning at the&nbsp;sensor&nbsp;front-end.</p>

<p>This paper is a key&nbsp;milestone in the&nbsp;research performed at the GREEN lab&nbsp;on&nbsp;intelligent and autonomous&nbsp;image sensors.&nbsp;This is also one of many key&nbsp;publications, and&nbsp;the second best paper award&nbsp;received by GREEN Lab students in 2016&nbsp;on this research topic. The research&nbsp;is supported in part by Mukhopadhyay&rsquo;s National Science&nbsp;Foundation CAREER Award and Office of Naval Research Young Investigator Award.</p>
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      <value>2016-10-26T00:00:00-04:00</value>
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      <value><![CDATA[Mohammad Faisal Amir, Duckhwan Kim, and Jae Ha Kung received the Best Student Paper Award at the IEEE SOI-3D-Subthreshold Microelectronics Technology Unified Conference (IEEE S3S), held October 10-13, 2016 in San Francisco, California. ]]></value>
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      <value><![CDATA[<p>ECE Ph.D. students&nbsp;Mohammad Faisal Amir, Duckhwan Kim, and Jae Ha Kung received the Best Student Paper Award at the IEEE SOI-3D-Subthreshold Microelectronics Technology Unified Conference (IEEE S3S), held October 10-13, 2016 in San Francisco, California.&nbsp;</p>
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            <title><![CDATA[Jae Ha Kung, Mohammad Faisal Amir, Duckhwan Kim, and Saibal Mukhopadhyay]]></title>
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      <email><![CDATA[jackie.nemeth@ece.gatech.edu]]></email>
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      <value><![CDATA[<p>Jackie Nemeth</p>

<p>School of Electrical and Computer Engineering</p>

<p>404-894-2906</p>
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