  Mobile devices can offload deep neural network (DNN)-based inference to the
cloud, overcoming local hardware and energy limitations. However, offloading
adds communication delay, thus increasing the overall inference time, and hence
it should be used only when needed. An approach to address this problem
consists of the use of adaptive model partitioning based on early-exit DNNs.
Accordingly, the inference starts at the mobile device, and an intermediate
layer estimates the accuracy: If the estimated accuracy is sufficient, the
device takes the inference decision; Otherwise, the remaining layers of the DNN
run at the cloud. Thus, the device offloads the inference to the cloud only if
it cannot classify a sample with high confidence. This offloading requires a
correct accuracy prediction at the device. Nevertheless, DNNs are typically
miscalibrated, providing overconfident decisions. This work shows that the
employment of a miscalibrated early-exit DNN for offloading via model
partitioning can significantly decrease inference accuracy. In contrast, we
argue that implementing a calibration algorithm prior to deployment can solve
this problem, allowing for more reliable offloading decisions.
