  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 4x in storage and computational complexity
without compromising performance. The source code is available at
https://github.com/intelligolabs/sparsity_sc_ee.
