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3D Convolutional Neural Networks are gaining increasing attention fromresearchers and practitioners and have found applications in many domains, suchas surveillance systems, autonomous vehicles, human monitoring systems, andvideo retrieval. However, their widespread adoption is hindered by their highcomputational and memory requirements, especially when resource-constrainedsystems are targeted. This paper addresses the problem of mapping X3D, astate-of-the-art model in Human Action Recognition that achieves accuracy of95.5\% in the UCF101 benchmark, onto any FPGA device. The proposed toolflowgenerates an optimised stream-based hardware system, taking into account theavailable resources and off-chip memory characteristics of the FPGA device. Thegenerated designs push further the current performance-accuracy pareto front,and enable for the first time the targeting of such complex model architecturesfor the Human Action Recognition task.

FMM-X3D: FPGA-based modeling and mapping of X3D for Human Action Recognition