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content="MTL-Split: Multi-Task Learning for Edge Devices using Split Computing paper website. Accepted @ DAC 2024."> | ||
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<title>MTL-Split: Multi-Task Learning for Edge Devices using Split Computing</title> | ||
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<h1 class="title is-1 publication-title">MTL-Split: Multi-Task Learning for Edge Devices using Split Computing</h1> | ||
<div class="is-size-5 publication-authors"> | ||
<span class="author-block"> | ||
<a href="https://www.capogrosso.eu">Luigi Capogrosso</a><sup>1</sup>, | ||
</span> | ||
<span class="author-block"> | ||
<a href="https://www.dimi.univr.it/?ent=persona&id=22371&lang=en">Enrico Fraccaroli</a><sup>1,2</sup>, | ||
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<span class="author-block"> | ||
<a href="https://cs.unc.edu/person/samarjit-chakraborty/">Samarjit Chakraborty</a><sup>1,2</sup>, | ||
</span> | ||
<span class="author-block"> | ||
<a href="https://www.dimi.univr.it/?ent=persona&id=59&lang=en">Franco Fummi</a><sup>1</sup>, | ||
</span> | ||
<span class="author-block"> | ||
<a href="https://www.dimi.univr.it/?ent=persona&id=218&lang=en">Marco Cristani</a><sup>1</sup>, | ||
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<sup>1</sup> | ||
Department of Engineering for Innovation Medicine, University of Verona, Italy | ||
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<sup>2</sup> | ||
Department of Computer Science, The University of North Carolina at Chapel Hill, USA | ||
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<h2 class="title is-3">Abstract</h2> | ||
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<p> | ||
Split Computing (SC), where a Deep Neural Network (DNN) is intelligently split with a part of it deployed on an edge device and the rest on a remote server is emerging as a promising approach. | ||
It allows the power of DNNs to be leveraged for latency-sensitive applications that do not allow the entire DNN to be deployed remotely, while not having sufficient computation bandwidth available locally. | ||
In many such embedded systems scenarios, such as those in the automotive domain, computational resource constraints also necessitate Multi-Task Learning (MTL), where the same DNN is used for multiple inference tasks instead of having dedicated DNNs for each ask, which would need more computing bandwidth. | ||
However, how to partition such a multi-tasking DNN to be deployed within a SC framework has not been sufficiently studied. | ||
This paper studies this problem, and MTL-Split, our novel proposed architecture, shows encouraging results on both synthetic and real-world data. | ||
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TBA. | ||
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The source code is based on <a href="https://github.com/nerfies/nerfies.github.io">Nerfies</a>. | ||
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