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submission.html
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<!doctype html>
<html>
<head>
<title>Fast ML for Science @ ICCAD 2023</title>
<meta charset="utf-8" name="viewport" content="width=device-width, initial-scale=1">
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<link rel="canonical" href="https://fastmachinelearning.org/iccad2023">
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<body>
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<!--Menu and Banner-->
<div class="menu-container"></div>
<div class="site-blocks-cover overlay inner-page-cover" style="background-image: url('image/sanfrancisco.jpg');"
data-stellar-background-ratio="0.5">
<div class="container">
<div class="row align-items-center justify-content-center">
<div class="col-md-10 text-center" data-aos="fade-up">
<h1>Paper Submission</h1>
</div>
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</div>
</div>
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<!--Start Overview-->
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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.
</p>
</div>
</div>
<!--Start Topics-->
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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>
</div>
</div>
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<div class="content-container">
<div class="content">
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<h2 class="add-top-margin">Important Dates</h2>
<hr>
<ul>
<li>Paper Submission Deadline: <s>September 23, 2023 AOE</s> September 30, 2023 AoE</li>
<li>Notification: <s>October 20, 2023</s> October 11, 2023 </li>
<li>Camera-Ready: <s>October 27, 2023</s> October 30, 2023 AoE </li>
<li>Workshop: November 2, 2023</li>
</ul>
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<h2 class="add-top-margin">Format</h2>
<hr>
<p class="text">
We welcome three types of submissions:
</p>
<ol class="nested">
<li>Technical papers with evaluation results</li>
<li>Position papers on directions for research and development</li>
<li>Review papers</li>
</ol>
<p class="text">Each technical/position/review paper must be 4-6 pages (not including references),
double-columned, 9pt, or 10pt font. All
accepted papers will be invited to present at the workshop.</p>
<p class="text">Please submit papers (in PDFs) via <a
href="https://fastml-iccad-23.hotcrp.com">HOTCRP</a>. We recommend the papers (.pdf format) follow
the
<a href="https://www.acm.org/publications/proceedings-template">IEEE Template</a>. For example, here
is a template on
<a
href="https://www.overleaf.com/latex/templates/ieee-bare-demo-template-for-conferences/ypypvwjmvtdf">overleaf</a>.
</div>
</div>
<div class="flex-row">
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<h2 class="add-top-margin">Organizers</h2>
<hr>
<ul>
<li>Nhan Tran, Fermilab and Northwestern University, USA</li>
<li>Paolo D'Alberto, AMD, USA</li>
<li>Javier Duarte, UC San Diego, USA</li>
<li>Ryan Kastner, UC San Diego, USA</li>
<li>Miaoyuan Liu, Purdue University, USA</li>
<li>Seda Ogrenci, Northwestern University, USA</li>
</ul>
<p class="text">
Contact: ntran at fnal dot gov, jduarte at ucsd dot edu
</p>
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<p>© Fast Machine Learning for Science @ ICCAD, 2023</p>
</div>
</div>
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