  Embedded distributed inference of Neural Networks has emerged as a promising
approach for deploying machine-learning models on resource-constrained devices
in an efficient and scalable manner. The inference task is distributed across a
network of embedded devices, with each device contributing to the overall
computation by performing a portion of the workload. In some cases, more
powerful devices such as edge or cloud servers can be part of the system to be
responsible of the most demanding layers of the network. As the demand for
intelligent systems and the complexity of the deployed neural network models
increases, this approach is becoming more relevant in a variety of applications
such as robotics, autonomous vehicles, smart cities, Industry 4.0 and smart
health. We present a systematic review of papers published during the last six
years which describe techniques and methods to distribute Neural Networks
across these kind of systems. We provide an overview of the current
state-of-the-art by analysing more than 100 papers, present a new taxonomy to
characterize them, and discuss trends and challenges in the field.
