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：对于人体动作识别任务（HAR），三维卷积神经网络已被证明具有卓越性能，取得了最先进的结果。本研究提出一种基于新型流式架构的工具流，通过综合考虑模型固有特征与目标FPGA设备特性，将此类模型映射至FPGA。HARFLOW3D工具流以ONNX格式的3D CNN模型及FPGA特性描述作为输入，生成可最小化计算延迟的设计方案。该工具流包含多个模块：i) 3D CNN解析器，ii) 性能与资源模型，iii) 面向生成硬件的3D模型调度算法，iv) 专为3D模型设计的资源感知优化引擎，v) 至FPGA可综合代码的自动映射。通过多种3D CNN与FPGA系统组合的实验，验证了该工具流对广泛模型与设备的支持能力。此外，工具流成功为先前未部署至FPGA的3D CNN模型生成了高性能结果，展现了FPGA系统在该领域的应用潜力。总体而言，HARFLOW3D工具流在延迟指标上可与多种手工优化方案相抗衡，相较现有部分工作可实现高达5倍的性能提升。