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<!doctype html>
<html>
<head>
<title>Fast ML for Science @ ICCAD 2023</title>
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<div class="container">
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<p></p>
<h1>Fast Machine Learning for Science Workshop</h1>
<hr>
<h5>Co-located with 2023 <u><a href="https://iccad.com/" style="color: white">International Conference on
Computer-Aided Design (ICCAD)</a></u> </h5>
<h4>Date: November 2, 2023</h4>
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<h2 class="add-top-margin">Overview</h2>
<hr>
<p class="text text-justify">
This workshop aims to address emerging challenges and explore innovative solutions in the field of
computer-aided design (CAD)
for integrated circuits and systems for ultra low latency and high bandwidth scientific applications.
The workshop builds on the ideas laid out in the <a
href="https://doi.org/10.3389/fdata.2022.787421">"Applications and Techniques for Fast Machine
Learning in Science"</a> white paper
and the corresponding Fast Machine Learning for Science conference series (<a
href="https://indico.cern.ch/event/1283970/">2023 edition</a>).
This workshop at ICCAD 2023 aims to bring domains together and forge new connections with the CAD
community.
</p>
<p class="text text-justify">
Scientific applications across particle physics, astrophysics, material sciences, quantum information
sciences, fusion energy (and beyond!)
utilize data acquisition and in situ processing systems which require very low latency and high data
bandwidth custom processing elements
and real-time control modules. Integrating data reduction and control applications with real-time
machine learning algorithms can enable significant
breakthroughs in the sciences. We will bring together researchers, practitioners, and industry experts
to exchange ideas, share applications,
and discuss the latest advancements in CAD methodologies, algorithms, and tools.
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<h2 class="add-top-margin">Topics of interest</h2>
<hr>
<p class="text">The areas of interest related to real-time scientific applications include but are not
limited to:</p>
<ul>
<li>Methods and tools for efficient algorithm design, implementation, and integration methodologies</li>
<li>Software-hardware codesign, partitioning, and optimizations</li>
<li>Design automation and synthesis, timing analysis</li>
<li>Physical design and layout</li>
<li>High-level synthesis and system-level design for edge AI hardware</li>
<li>Robust machine learning, anomaly detection, and fault tolerance</li>
<li>Continuous, adaptive, and reinforcement learning for low latency control</li>
<li>Emerging technologies in CAD machine learning and AI-assisted design</li>
</ul>
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<p class="text">
Contact: ntran at fnal dot gov, jduarte at ucsd dot edu
</p>
<p class="text">
Please follow us on Twitter <a href="https://twitter.com/fastmlscience">@fastmlscience</a>
</p>
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<p>© Fast Machine Learning for Science @ ICCAD, 2023</p>
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