In this paper, we evaluate the use of a trained Long Short-Term Memory (LSTM) network as a surrogate for a Euler-Bernoulli beam model, and then we describe and characterize an FPGA-based deployment of the model for use in real-time structural health monitoring applications. The focus of our efforts is the DROPBEAR (Dynamic Reproduction of Projectiles in Ballistic Environments for Advanced Research) dataset, which was generated as a benchmark for the study of real-time structural modeling applications. The purpose of DROPBEAR is to evaluate models that take vibration data as input and give the initial conditions of the cantilever beam on which the measurements were taken as output. DROPBEAR is meant to serve an exemplar for emerging high-rate "active structures" that can be actively controlled with feedback latencies of less than one microsecond. Although the Euler-Bernoulli beam model is a well-known solution to this modeling problem, its computational cost is prohibitive for the time scales of interest. It has been previously shown that a properly structured LSTM network can achieve comparable accuracy with less workload, but achieving sub-microsecond model latency remains a challenge. Our approach is to deploy the LSTM optimized specifically for latency on FPGA. We designed the model using both high-level synthesis (HLS) and hardware description language (HDL). The lowest latency of 1.42 $\mu$S and the highest throughput of 7.87 Gops/s were achieved on Alveo U55C platform for HDL design.

翻译：本文评估了使用训练后的长短期记忆网络（LSTM）作为欧拉-伯努利梁模型的替代方案，进而描述并表征了该模型在现场可编程门阵列（FPGA）上的部署，以用于实时结构健康监测应用。我们的工作聚焦于DROPBEAR（弹道环境中的弹丸动态再现高级研究）数据集，该数据集作为实时结构建模应用研究的基准而生成。DROPBEAR旨在评估以振动数据为输入、以测量悬臂梁初始条件为输出的模型。它旨在作为新兴高速“主动结构”的范例，这类结构能够以小于1微秒的反馈延迟进行主动控制。尽管欧拉-伯努利梁模型是该建模问题的经典解，但其计算成本在感兴趣的时间尺度上难以承受。已有研究表明，恰当结构的LSTM网络能以更少的工作量实现同等精度，但实现亚微秒级的模型延迟仍具挑战。我们的方法是将针对延迟优化的LSTM部署于FPGA，并通过高层次综合（HLS）和硬件描述语言（HDL）分别设计模型。在Alveo U55C平台上，HDL设计实现了最低1.42μS的延迟和最高7.87 Gops/s的吞吐量。