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+ + 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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