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<i><span style="font-size: 12pt; font-family: Helvetica; color: rgb(139, 1, 2);">UNIVERSITY OF CHICAGO</span></i><o:p></o:p></p>
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<i><span style="font-size: 12pt; font-family: Helvetica; color: rgb(139, 1, 2);">COMPUTER SCIENCE DEPARTMENT</span></i><o:p></o:p></p>
<div style="font-family: Helvetica;"><i style="font-family: Calibri, sans-serif; font-size: 10pt;"><span style="font-size: 12pt; font-family: Helvetica; color: rgb(139, 1, 2);">PRESENTS</span></i></div>
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<b style="font-size: 14px;">Olivia Weng</b></div>
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<b style="font-size: 14px;">University of California-San Diego </b></div>
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<b style="font-size: 14px;">Ph.D Candidate </b></div>
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<b style="font-size: 14px;">Thursday, October 10th </b></div>
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<b style="font-size: 14px;">10:00 am - 11:00 am </b></div>
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<b style="font-size: 14px;"><font color="#3c4043">In Person: </font>John Crerar Library Rm 298</b></div>
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<span style="font-size: 14px;"><b>Zoom: </b><a href="https://urldefense.com/v3/__https://uchicago.zoom.us/j/99851094694?pwd=I5jSuDaagg9yqdRNjPrAuFoCzzNxP5.1__;!!BpyFHLRN4TMTrA!8pXPeVvqWsDuR58cw1HKkJtAL9GOXQDVVo6-c84V_CHUftlVChfIh9Wyep3mB7IYXux796vQnatebh7ERdylmvg7BrE1Su7QPEszZw$">https://uchicago.zoom.us/j/99851094694?pwd=I5jSuDaagg9yqdRNjPrAuFoCzzNxP5.1</a></span></div>
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<span style="font-size: 14px;">Meeting ID: 998 5109 4694</span></div>
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<span style="font-size: 14px;">Passcode: 671006</span></div>
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<b style="font-size: 14px;">Title: Efficient and Resilient Neural Networks for On-chip Inference<br>
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<span style="font-size: 14px;"><b>Abstract:</b> <span style="font-family: Arial, sans-serif; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-origin: initial; background-clip: initial;">Scientific
applications are increasingly using neural networks (NNs) at the edge as a fundamental tool for advancing fields such as particle physics and materials science. As the scientific instruments used in these experiments become more advanced, they produce a lot
more data (e.g., 40 TB/s) than before. As a result, scientists are relying on edge NNs, which have more capabilities than traditional algorithms, to process the data. To process data quickly enough, these scientific edge NNS have unique requirements. They
must (1) be heavily quantized and (2) execute fully on chip. Even more so, these scientific NNs often operate in high radiation environments (1000X that of space). My talk focuses on using hardware-software co-design to create efficient, fault-tolerant computer
architectures for NNs that execute fully on chip so that they meet the strict latency and throughput requirements laid out by these scientific experiments to advance research in their fields. I will talk about two projects: (1) scaling up lookup-table-based
NNs and (2) codesigning fault-tolerant edge NNs. This work provides insights and tradeoffs that will help scientists better run their NNs on specialized hardware such as FPGAs and ASICs to successfully perform their experiments.</span></span></div>
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<div><span style="font-size: 14px;"><b>Bio:</b> Olivia Weng is a PhD candidate in Computer Science and Engineering working with Professor Ryan Kastner at UC San Diego. She received her BS in Computer Science at the University of Chicago. Her research focuses
on using hardware-software co-design to create efficient, fault-tolerant computer architectures for machine learning. Her work has led to collaborations with researchers at AMD as well as high energy physicists at Fermilab and CERN who seek to deploy machine
learning at the edge to discover new physics.</span></div>
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<b>Host: Yanjing Li </b></div>
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