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.

研究提出一种工具流程，将 3D CNN 模型优化到 FPGA 设备上，采用同步数据流图来模拟设计并引入转换来拓展和探索设计空间，以实现高吞吐量设计。在多个 FPGA 设备上评估了各种 3D CNN 模型，证明了与早期手动调整和特定模型的设计相比，该工具流程具有竞争性的性能。

fpgaHART: 一种用于将 3D CNNs 加速到 FPGA 上的吞吐量导向的 HAR 工具流