  Surveillance systems, autonomous vehicles, human monitoring systems, and
video retrieval are just few of the many applications in which 3D Convolutional
Neural Networks are exploited. However, their extensive use is restricted by
their high computational and memory requirements, especially when integrated
into systems with limited resources. This study proposes a toolflow that
optimises the mapping of 3D CNN models for Human Action Recognition onto FPGA
devices, taking into account FPGA resources and off-chip memory
characteristics. The proposed system employs Synchronous Dataflow (SDF) graphs
to model the designs and introduces transformations to expand and explore the
design space, resulting in high-throughput designs. A variety of 3D CNN models
were evaluated using the proposed toolflow on multiple FPGA devices,
demonstrating its potential to deliver competitive performance compared to
earlier hand-tuned and model-specific designs.
