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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qjrrvry5ubgdlarkymvlxuip6m" style="color: black;">2018 Design, Automation & Test in Europe Conference & Exhibition (DATE)</a>
The number of connected Internet of Things (IoT) devices are expected to reach over 20 billion by 2020. These range from basic sensor nodes that log and report the data to the ones that are capable of processing the incoming information and taking an action accordingly. Machine learning, and in particular deep learning, is the de facto processing paradigm for intelligently processing these immense volumes of data. However, the resource inhibited environment of IoT devices, owing to their<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.23919/date.2018.8342120">doi:10.23919/date.2018.8342120</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/date/0001TBHKHR18.html">dblp:conf/date/0001TBHKHR18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jb6ouvhtrrevlj3dehuyj6inaq">fatcat:jb6ouvhtrrevlj3dehuyj6inaq</a> </span>
more »... energy budget and low compute capabilities, render them a challenging platform for deployment of desired data analytics. This paper provides an overview of the current and emerging trends in designing highly efficient, reliable, secure and scalable machine learning architectures for such devices. The paper highlights the focal challenges and obstacles being faced by the community in achieving its desired goals. The paper further presents a roadmap that can help in addressing the highlighted challenges and thereby designing scalable, high-performance, and energy efficient architectures for performing machine learning on the edge.
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