  This work makes a substantial step in the field of split computing, i.e., how
to split a deep neural network to host its early part on an embedded device and
the rest on a server. So far, potential split locations have been identified
exploiting uniquely architectural aspects, i.e., based on the layer sizes.
Under this paradigm, the efficacy of the split in terms of accuracy can be
evaluated only after having performed the split and retrained the entire
pipeline, making an exhaustive evaluation of all the plausible splitting points
prohibitive in terms of time. Here we show that not only the architecture of
the layers does matter, but the importance of the neurons contained therein
too. A neuron is important if its gradient with respect to the correct class
decision is high. It follows that a split should be applied right after a layer
with a high density of important neurons, in order to preserve the information
flowing until then. Upon this idea, we propose Interpretable Split (I-SPLIT): a
procedure that identifies the most suitable splitting points by providing a
reliable prediction on how well this split will perform in terms of
classification accuracy, beforehand of its effective implementation. As a
further major contribution of I-SPLIT, we show that the best choice for the
splitting point on a multiclass categorization problem depends also on which
specific classes the network has to deal with. Exhaustive experiments have been
carried out on two networks, VGG16 and ResNet-50, and three datasets,
Tiny-Imagenet-200, notMNIST, and Chest X-Ray Pneumonia. The source code is
available at https://github.com/vips4/I-Split.
