  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.
