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| <!DOCTYPE html> | ||
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| content="LO-SC: Local-only Split Computing for Accurate Deep Learning on Edge Devices. Accepted @ VLSI Design 2025."> | ||
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| <h1 class="title is-1 publication-title">LO-SC: Local-only Split Computing for Accurate Deep Learning on Edge Devices</h1> | ||
| <div class="is-size-5 publication-authors"> | ||
| <span class="author-block"> | ||
| <a target="_blank" href="https://www.capogrosso.eu">Luigi Capogrosso</a><sup>1</sup>, | ||
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| <span class="author-block"> | ||
| <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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| <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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| <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://cs.unc.edu/person/samarjit-chakraborty/">Samarjit Chakraborty</a><sup>2</sup> | ||
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| <sup>1</sup> | ||
| Department of Engineering for Innovation Medicine, University of Verona, Italy | ||
| </span> | ||
| <span class="author-block"> | ||
| <sup>2</sup> | ||
| Department of Computer Science, The University of North Carolina at Chapel Hill, USA | ||
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| <span class="tag is-success is-primary is-large">🎉 Accepted @ VLSI Design 2025 🎉</span> | ||
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| <h6 class="subtitle is-6 has-text-centered"> | ||
| Traditional SC approach versus the proposed LO-SC architecture. | ||
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| <h2 class="title is-3">Abstract</h2> | ||
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| <p> | ||
| Split Computing (SC) enables deploying a Deep Neural Network (DNN) on edge devices with limited resources by splitting the workload between the edge device and a remote server. | ||
| However, relying on a server can be expensive, requires a reliable network, and introduces unpredictable latency. | ||
| Existing solutions for on-device DNNs deployment often sacrifice accuracy for efficiency. | ||
| In this paper, we study how to use the concepts from SC to split a DNN for executing on the same device without compromising accuracy. | ||
| In other words, we propose Local-Only Split Computing (LO-SC), a new approach to split a DNN for execution entirely on the edge device while maintaining high accuracy and predictable latency. | ||
| We formalize LO-SC as a Mixed-Integer Linear Problem (MILP) problem and solve it using a multi-constrained ordered knapsack algorithm. | ||
| The proposed method achieves promising results on both synthetic and real-world data, offering a viable alternative for accurately deploying DNNs on resource-constrained edge devices. | ||
| </p> | ||
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| <pre><code>TBA. | ||
| </code></pre> | ||
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