Merge pull request #3 from fastmachinelearning/nvt_cfp_2

update for the CFP
fastmachinelearning · Jul 31, 2023 · d7b7806 · d7b7806
2 parents 6c905d0 + ea63146
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diff --git a/committee.html b/committee.html
@@ -7,6 +7,7 @@
   <link href="css/frame.css" media="screen" rel="stylesheet" type="text/css" />
   <link href="css/controls.css" media="screen" rel="stylesheet" type="text/css" />
   <link href="css/custom.css" media="screen" rel="stylesheet" type="text/css" />
+  <link rel="canonical" href="https://fastmachinelearning.org/iccad2023">
   <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css">
   <link href='https://fonts.googleapis.com/css?family=Open+Sans:400,700' rel='stylesheet' type='text/css'>
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diff --git a/program.html b/program.html
@@ -7,6 +7,7 @@
   <link href="css/frame.css" media="screen" rel="stylesheet" type="text/css" />
   <link href="css/controls.css" media="screen" rel="stylesheet" type="text/css" />
   <link href="css/custom.css" media="screen" rel="stylesheet" type="text/css" />
+  <link rel="canonical" href="https://fastmachinelearning.org/iccad2023">
   <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css">
   <link href='https://fonts.googleapis.com/css?family=Open+Sans:400,700' rel='stylesheet' type='text/css'>
   <link href='https://fonts.googleapis.com/css?family=Open+Sans+Condensed:300,700' rel='stylesheet' type='text/css'>

diff --git a/submission.html b/submission.html
@@ -7,6 +7,7 @@
   <link href="css/frame.css" media="screen" rel="stylesheet" type="text/css" />
   <link href="css/controls.css" media="screen" rel="stylesheet" type="text/css" />
   <link href="css/custom.css" media="screen" rel="stylesheet" type="text/css" />
+  <link rel="canonical" href="https://fastmachinelearning.org/iccad2023">
   <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css">
   <link href='https://fonts.googleapis.com/css?family=Open+Sans:400,700' rel='stylesheet' type='text/css'>
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@@ -36,6 +37,50 @@ <h1>Paper Submission</h1>
      </div>
   </div>
   </div> 
+    <!-------------------------------------------------------------------------------------------->
+    <div class="content-container">
+      <div class="content">
+        <div class="content-table flex-column">
+          <div class="flex-row">
+            <!--Start Overview-->
+            <div class="flex-item flex-column">
+              <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-->
+            <div class="flex-row">
+            <div class="flex-item flex-column">
+              <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>
+          </div>
   <!-------------------------------------------------------------------------------------------->
     <div class="content-container">
     <div class="content">
@@ -68,11 +113,29 @@ <h2 class="add-top-margin">Format</h2>
             <p class="text">Please submit papers (in PDFs) via <a href="https://fastml-iccad-23.hotcrp.com">HOTCRP</a>. The papers (.pdf format) should follow the <a href="https://www.acm.org/publications/proceedings-template">ACM Template</a>. <a href="https://www.overleaf.com/latex/templates/association-for-computing-machinery-acm-sig-proceedings-template/bmvfhcdnxfty">Here</a> is a paper sample.
           </div>
         </div>
+        <div class="flex-row">
+          <div class="flex-item flex-column">
+            <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
+            </p>
+          </div>
+          </div>  
         <div class="flex-row">
           <div class="flex-item flex-column">
             <p>© Fast Machine Learning for Science @ ICCAD, 2023</p>
           </div>
         </div>
+
       </div>
     </div>
   </div>