  Collaborative inference systems are one of the emerging solutions for
deploying deep neural networks (DNNs) at the wireless network edge. Their main
idea is to divide a DNN into two parts, where the first is shallow enough to be
reliably executed at edge devices of limited computational power, while the
second part is executed at an edge server with higher computational
capabilities. The main advantage of such systems is that the input of the DNN
gets compressed as the subsequent layers of the shallow part extract only the
information necessary for the task. As a result, significant communication
savings can be achieved compared to transmitting raw input samples. In this
work, we study early exiting in the context of collaborative inference, which
allows obtaining inference results at the edge device for certain samples,
without the need to transmit the partially processed data to the edge server at
all, leading to further communication savings. The central part of our system
is the transmission-decision (TD) mechanism, which, given the information from
the early exit, and the wireless channel conditions, decides whether to keep
the early exit prediction or transmit the data to the edge server for further
processing. In this paper, we evaluate various TD mechanisms and show
experimentally, that for an image classification task over the wireless edge,
proper utilization of early exits can provide both performance gains and
significant communication savings.
