  3D Convolutional Neural Networks are gaining increasing attention from
researchers and practitioners and have found applications in many domains, such
as surveillance systems, autonomous vehicles, human monitoring systems, and
video retrieval. However, their widespread adoption is hindered by their high
computational and memory requirements, especially when resource-constrained
systems are targeted. This paper addresses the problem of mapping X3D, a
state-of-the-art model in Human Action Recognition that achieves accuracy of
95.5\% in the UCF101 benchmark, onto any FPGA device. The proposed toolflow
generates an optimised stream-based hardware system, taking into account the
available resources and off-chip memory characteristics of the FPGA device. The
generated designs push further the current performance-accuracy pareto front,
and enable for the first time the targeting of such complex model architectures
for the Human Action Recognition task.
