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：针对人体动作识别任务（HAR），三维卷积神经网络已被证明具有极高有效性，并取得了当前最佳结果。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上。HARFLOW3D工具流以ONNX格式的3D CNN模型及FPGA特性描述文件为输入，生成使计算延迟最小化的设计方案。该工具流包含多个组成部分：（i）3D CNN解析器；（ii）性能与资源模型；（iii）用于在生成硬件上执行3D模型的调度算法；（iv）面向3D模型优化的资源感知引擎；（v）可综合FPGA代码的自动映射模块。通过在多种3D CNN与FPGA系统组合上的实验，验证了该工具流对广泛模型与设备的支持能力。此外，该工具流对先前未映射至FPGA的3D CNN模型取得了高性能结果，展现了FPGA系统在该领域的应用潜力。总体而言，HARFLOW3D展现了其与多种手工优化方法竞争的能力，相较于现有部分工作可提升高达5倍的性能。