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hac.html
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<!DOCTYPE HTML>
<!--
Editorial by HTML5 UP
html5up.net | @ajlkn
Free for personal and commercial use under the CCA 3.0 license (html5up.net/license)
-->
<html>
<head>
<title>A3D3</title>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=no" />
<link rel="stylesheet" href="assets/css/main.css" />
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<!-- Header -->
<header id="header">
<a href="index.html" class="logo"><img src="images/a3d3logo2.png" alt="A3D3"
width=200></img></a>
<ul class="icons">
<li><a href="https://twitter.com/a3d3institute" class="icon brands fa-twitter"><span
class="label">Twitter</span></a></li>
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</header>
<!-- Content -->
<section>
<header class="main">
<h1>Hardware and Algorithm Codevelopment</h1>
</header>
<!--
<span class="image main"><img src="images/pic11.jpg" alt="" /></span>
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<p>
Developing AI methods to encode non-lattice-structured
data is one main challenge in current AI systems. Such data structures are common in the
scientific applications to be explored in A3D3. AI researchers have to carefully consider
the specifics of the data irregularity, the chosen model architecture, and the system
constraints. A3D3 proposes to investigate optimal encoders for these non-lattice-structured
scientific data.
Within the A3D3 science drivers, fast identification of sporadic events from large-scale
data streams provides a unique opportunity for discovery.Traditional selection strategies
use either subjective heuristics (e.g., a high energy collision or a bright transient) or
model-based classifiers that are trained with specific labels. Such strategies exclude
unconventional signatures with unexpected properties where manually defined heuristics and
labeled examples are insufficient to explain them. Decoders based on encoded data can be
designed to overcome these challenges. A3D3 will explore semi-supervised/unsupervised
learning methods for e↵ective anomaly, or out-of-class, detection. Recent work on
variational autoencoders (VAEs) and generative models learn the distribution of the input
data and detect anomalies based on their likelihood scores.
Hardware-AI co-design is the tight coupling of the design of AI algorithms with the domain
science, hardware, and system constraints.
A hallmark of co-design is either an encoding of the design constraints directly in the cost
function of the training algorithm or a fast-to-evaluate approximation of the acceptability
of an on-device solution to allow for fast exploration and evaluation.
Our primary focus is on achieving low latency, real-time processing of scientific data. A3D3
aims to design end-to-end workflows integrated with user-friendly tools for hardware-aware
co-design of AI algorithms.
</section>
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<li><a href="index.html">Home</a></li>
<li><a href="about.html">About</a></li>
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<li><a href="researchers.html">Researchers</a></li>
<li><a href="trainees.html">Trainees</a></li>
<li><a href="institutions.html">Institutions</a></li>
<li><a href="ExecBoard.html">Executive Board</a></li>
<li><a href="advisory.html">Advisory board</a></li>
<li><a href="collaborators.html">Collaborators</a></li>
</ul>
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<span class="opener">Activities</span>
<ul>
<li><a href="hac.html">HAC</a></li>
<li><a href="hep.html">HEP</a></li>
<li><a href="mma.html">MMA</a></li>
<li><a href="neuros.html">Neuroscience</a></li>
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