  The potential held by the gargantuan volumes of data being generated across
networks worldwide has been truly unlocked by machine learning techniques and
more recently Deep Learning. The advantages offered by the latter have seen it
rapidly becoming a framework of choice for various applications. However, the
centralization of computational resources and the need for data aggregation
have long been limiting factors in the democratization of Deep Learning
applications. Edge Computing is an emerging paradigm that aims to utilize the
hitherto untapped processing resources available at the network periphery. Edge
Intelligence (EI) has quickly emerged as a powerful alternative to enable
learning using the concepts of Edge Computing. Deep Learning-based Edge
Intelligence or Deep Edge Intelligence (DEI) lies in this rapidly evolving
domain. In this article, we provide an overview of the major constraints in
operationalizing DEI. The major research avenues in DEI have been consolidated
under Federated Learning, Distributed Computation, Compression Schemes and
Conditional Computation. We also present some of the prevalent challenges and
highlight prospective research avenues.
