  Many recent pattern recognition applications rely on complex distributed
architectures in which sensing and computational nodes interact together
through a communication network. Deep neural networks (DNNs) play an important
role in this scenario, furnishing powerful decision mechanisms, at the price of
a high computational effort. Consequently, powerful state-of-the-art DNNs are
frequently split over various computational nodes, e.g., a first part stays on
an embedded device and the rest on a server. Deciding where to split a DNN is a
challenge in itself, making the design of deep learning applications even more
complicated. Therefore, we propose Split-Et-Impera, a novel and practical
framework that i) determines the set of the best-split points of a neural
network based on deep network interpretability principles without performing a
tedious try-and-test approach, ii) performs a communication-aware simulation
for the rapid evaluation of different neural network rearrangements, and iii)
suggests the best match between the quality of service requirements of the
application and the performance in terms of accuracy and latency time.
