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.

翻译：监控系统、自动驾驶车辆、人体监测系统以及视频检索仅仅是3D卷积神经网络应用的少数场景。然而，其高昂的计算和内存需求限制了其广泛应用，尤其是在集成到资源受限的系统时。本研究提出了一种工具流，该工具流针对FPGA器件上人体动作识别的3D CNN模型映射进行优化，充分考虑了FPGA资源和片外存储器特性。所提出的系统采用同步数据流图对设计进行建模，并引入转换方法以扩展和探索设计空间，最终实现高吞吐量设计。使用该工具流在多个FPGA器件上评估了多种3D CNN模型，结果表明与先前的手工调优及模型专用设计相比，该工具流具有提供竞争性性能的潜力。