  Almost in every heavily computation-dependent application, from 6G
communication systems to autonomous driving platforms, a large portion of
computing should be near to the client side. Edge computing (AI at Edge) in
mobile devices is one of the optimized approaches for addressing this
requirement. Therefore, in this work, the possibilities and challenges of
implementing a low-latency and power-optimized smart mobile system are
examined. Utilizing Field Programmable Gate Array (FPGA) based solutions at the
edge will lead to bandwidth-optimized designs and as a consequence can boost
the computational effectiveness at a system-level deadline. Moreover, various
performance aspects and implementation feasibilities of Neural Networks (NNs)
on both embedded FPGA edge devices (using Xilinx Multiprocessor System on Chip
(MPSoC)) and Cloud are discussed throughout this research. The main goal of
this work is to demonstrate a hybrid system that uses the deep learning
programmable engine developed by Xilinx Inc. as the main component of the
hardware accelerator. Then based on this design, an efficient system for mobile
edge computing is represented by utilizing an embedded solution.
