For Human Action Recognition tasks (HAR), 3D Convolutional Neural Networks have proven to be highly effective, achieving state-of-the-art results. This study introduces a novel streaming architecture based toolflow for mapping such models onto FPGAs considering the model's inherent characteristics and the features of the targeted FPGA device. The HARFLOW3D toolflow takes as input a 3D CNN in ONNX format and a description of the FPGA characteristics, generating a design that minimizes the latency of the computation. The toolflow is comprised of a number of parts, including i) a 3D CNN parser, ii) a performance and resource model, iii) a scheduling algorithm for executing 3D models on the generated hardware, iv) a resource-aware optimization engine tailored for 3D models, v) an automated mapping to synthesizable code for FPGAs. The ability of the toolflow to support a broad range of models and devices is shown through a number of experiments on various 3D CNN and FPGA system pairs. Furthermore, the toolflow has produced high-performing results for 3D CNN models that have not been mapped to FPGAs before, demonstrating the potential of FPGA-based systems in this space. Overall, HARFLOW3D has demonstrated its ability to deliver competitive latency compared to a range of state-of-the-art hand-tuned approaches being able to achieve up to 5$\times$ better performance compared to some of the existing works.

翻译：对于人体动作识别（HAR）任务，三维卷积神经网络已被证明具有极高有效性，能够达到最先进水平。本研究提出了一种基于新型流式架构的工具流，将此类模型映射到FPGA上，同时考虑了模型自身特性和目标FPGA设备的特征。HARFLOW3D工具流以ONNX格式的3D-CNN和FPGA特性描述为输入，生成最小化计算延迟的设计方案。该工具流包含以下组件：i) 3D-CNN解析器，ii) 性能与资源模型，iii) 在生成硬件上执行3D模型的调度算法，iv) 专为3D模型定制的资源感知优化引擎，v) 自动映射至FPGA可综合代码。通过在多种3D-CNN与FPGA系统组合上的系列实验，验证了该工具流支持广泛模型与设备的能力。此外，该工具流针对此前未被映射至FPGA的3D-CNN模型取得了高性能结果，展现了基于FPGA系统在该领域的潜力。总体而言，HARFLOW3D证明其能够提供与多种最先进手工调优方法相竞争的延迟性能，相比现有部分研究工作可实现高达5倍的性能提升。