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| <h1 class="title is-1 publication-title">Enhancing Split Computing and Early Exit Applications through Predefined Sparsity</h1> | ||
| <div class="is-size-5 publication-authors"> | ||
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| <a target="_blank" href="https://www.capogrosso.eu">Luigi Capogrosso</a><sup>1</sup>, | ||
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| <a target="_blank" href="https://www.dimi.univr.it/?ent=persona&id=22371&lang=en">Enrico Fraccaroli</a><sup>1,2</sup>, | ||
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| <a target="_blank" href="">Giulio Petrozziello</a><sup>3</sup>, | ||
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| <a target="_blank" href="https://www.dimi.univr.it/?ent=persona&id=9250&lang=en">Francesco Setti</a>, | ||
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| <a target="_blank" href="https://cs.unc.edu/person/samarjit-chakraborty/">Samarjit Chakraborty</a><sup>1,2</sup>, | ||
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| <span class="author-block"> | ||
| <a target="_blank" href="https://www.dimi.univr.it/?ent=persona&id=59&lang=en">Franco Fummi</a><sup>1</sup>, | ||
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| <span class="author-block"> | ||
| <a target="_blank" 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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| <sup>3</sup> | ||
| Department of Computer Science, University of Verona, Italy | ||
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| <h6 class="subtitle is-6 has-text-centered"> | ||
| Starting from a DNN ${\mathcal{M}(\cdot{})}$, we first apply the <i>predefined sparsity</i>, and then we train the network. | ||
| After the training stage, we split the network following the SC and EE paradigm. | ||
| As a result, the final architecture is not so computationally intensive, doesn't require huge storage spaces, and has less energy consumption, all without compromising the overall performance. | ||
| </h6> | ||
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| <h2 class="title is-3">Abstract</h2> | ||
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| <p> | ||
| In the past decade, Deep Neural Networks (DNNs) achieved state-of-the-art performance in a broad range of problems, spanning from object classification and action recognition to smart building and healthcare. | ||
| The flexibility that makes DNNs such a pervasive technology comes at a price: the computational requirements preclude their deployment on most of the resource-constrained edge devices available today to solve real-time and real-world tasks. | ||
| This paper introduces a novel approach to address this challenge by combining the concept of predefined sparsity with Split Computing (SC) and Early Exit (EE). | ||
| In particular, SC aims at splitting a DNN with a part of it deployed on an edge device and the rest on a remote server. | ||
| Instead, EE allows the system to stop using the remote server and rely solely on the edge device's computation if the answer is already good enough. | ||
| Specifically, how to apply such a predefined sparsity to a SC and EE paradigm has never been studied. | ||
| This paper studies this problem and shows how predefined sparsity significantly reduces the computational, storage, and energy burdens during the training and inference phases, regardless of the hardware platform. | ||
| This makes it a valuable approach for enhancing the performance of SC and EE applications. | ||
| Experimental results showcase reductions exceeding $4\times{}$ in storage and computational complexity without compromising performance. | ||
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| <pre><code>TBA. | ||
| </code></pre> | ||
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