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Structures operating in high-rate dynamic environments, such as hypersonic vehicles, orbital space infrastructure, and blast mitigation systems, require microsecond (μs) decision-making. Advances in real-time sensing, edge-computing, and high-bandwidth computer memory are enabling emerging technologies such as High-rate structural health monitoring (HR-SHM) to become more feasible. Due to the time restrictions such systems operate under, a target of 1 millisecond (ms) from event detection to decision-making is set at the goal to enable HR-SHM. With minimizing latency in mind, a data-driven method that relies on time-series measurements processed in real-time to infer the state of the structure is investigated in this preliminary work. A methodology for deploying LSTM-based state estimators for structures using subsampled time-series vibration data is presented. The proposed estimator is deployed to an embedded real-time device and the achieved accuracy along with system timing are discussed. The proposed approach has shown potential for high-rate state estimation as it provides sufficient accuracy for the considered structure while a time-step of 2.5 ms is achieved. The Contributions of this work are twofold: 1) a framework for deploying LSTM models in real-time for high-rate state estimation, 2) an experimental validation of LSTMs running on a real-time computing system.

 

In this paper, a method for real-time forecasting of the dynamics of structures experiencing nonstationary inputs is described. This is presented as time series predictions across different timescales. The target applications include hypersonic vehicles, space launch systems, real-time prognostics, and monitoring of high-rate and energetic systems. This work presents numerical analysis and experimental results for the real-time implementation of a Fast Fourier Transform (FFT)-based approach for time series forecasting. For this preliminary study, a testbench structure that consists of a cantilever beam subjected to nonstationary inputs is used to generate experimental data. First, the data is de-trended, then the time series data is transferred into the frequency domain, and measures for frequency, amplitude, and phase are obtained. Thereafter, select frequency components are collected, transformed back to the time domain, recombined, and then the trend in the data is restored. Finally, the recombined signals are propagated into the future to the selected prediction horizon. This preliminary time series forecasting work is done offline using pre-recorded experimental data, and the FFT-based approach is implemented in a rolling window configuration. Here learning windows of 0.1, 0.5, and 1 s are considered with different computation times simulated. Results demonstrate that the proposed FFT-based approach can maintain a constant prediction horizon at 1 s with sufficient accuracy for the considered system. The performance of the system is quantified using a variety of metrics. Computational speed and prediction accuracy as a function of training time and learning window lengths are examined in this work. The algorithm configuration with the shortest learning window (0.1 s) is shown to converge faster following the nonstationary when compared to algorithm configuration with longer learning windows.

 

The dominance of machine learning and the ending of Moore’s law have renewed interests in Processor in Memory (PIM) architectures. This interest has produced several recent proposals to modify an FPGA’s BRAM architecture to form a next-generation PIM reconfigurable fabric [1], [2]. PIM architectures can also be realized within today’s FPGAs as overlays without the need to modify the underlying FPGA architecture. To date, there has been no study to understand the comparative advantages of the two approaches. In this paper, we present a study that explores the comparative advantages between two proposed custom architectures and a PIM overlay running on a commodity FPGA. We created PiCaSO, a Processor in/near Memory Scalable and Fast Overlay architecture as a representative PIM overlay. The results of this study show that the PiCaSO overlay achieves up to 80% of the peak throughput of the custom designs with 2.56× shorter latency and 25% – 43% better BRAM memory utilization efficiency. We then show how several key features of the PiCaSO overlay can be integrated into the custom PIM designs to further improve their throughput by 18%, latency by 19.5%, and memory efficiency by 6.2%. 

“Active structures” are physical structures that incorporate real-time monitoring and control. Examples includeactive vibration damping or blast mitigation systems. Evaluating physics-based models in real-time is generally not feasible for such systems having high-rate dynamics which require microsecond response times, but data-driven machine-learning-based models can potentially offer a solution. This paper compares the cost and performance of two FPGA-based implementations of real-time, continuously-trained models for forecasting timeseries signals with non-stationarities, with one using HighLevel Synthesis (HLS) and the other a programmable overlay architecture. The proposed model accepts a uni-variate vibration signal and seeks to forecast future samples to inform highrate controllers. The proposed forecasting method performs two concurrent neural inference operations. One inference forecasts the state of the signal f samples into the future as a function of the most recent h samples, while the other forecasts the current sample given h samples starting from h + f − 1 samples into the past. The first forecast produces the forecast while the second forecast allows the system to calculate the model’s loss and perform an immediate model update before the next sample period. 

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 µS and the highest throughput of 7.87 Gops/s were achieved on Alveo U55C platform for HDL design. 

Structures operating in high-rate dynamic environments, such as hypersonic vehicles, orbital space infrastructure, and blast mitigation systems, require microsecond (µs) decisionmaking. Advances in real-time sensing, edge-computing, and high-bandwidth computer memory are enabling emerging technologies such as High-rate structural health monitoring (HRSHM) to become more feasible. Due to the time restrictions such systems operate under, a target of 1 millisecond (ms) from event detection to decision-making is set at the goal to enable HRSHM. With minimizing latency in mind, a data-driven method that relies on time-series measurements processed in real-time to infer the state of the structure is investigated in this preliminary work. A methodology for deploying LSTM-based state estimators for structures using subsampled time-series vibration data is presented. The proposed estimator is deployed to an embedded real-time device and the achieved accuracy along with system timing are discussed. The proposed approach has shown potential for high-rate state estimation as it provides sufficient accuracy for the considered structure while a time-step of 2.5 ms is achieved. The Contributions of this work are twofold: 1) a framework for deploying LSTM models in real-time for high-rate state estimation, 2) an experimental validation of LSTMs running on a real-time computing system. 

The increasing density of distributed BRAMs diffused throughout modern Field Programmable Gate Arrays (FPGAs) is ideal for forming processor in/near memory architectures. This breaks the traditional von Neumann memory bottleneck limiting concurrency and degrading energy efficiency. Ideally, processing density should scale linearly with BRAM capacity, and clock frequencies should be set by the read/write access times of the BRAM. In this paper, we present a PIM overlay that achieves these goals. We observe an improvement of performance by 2.25×, logic resource utilization by 2×, and accumulation delay by 17× compared to prior published work.