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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.
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Microsecond time-resolved X-ray scattering by utilizing MHz repetition rate at second-generation XFELs
Abstract Detecting microsecond structural perturbations in biomolecules has wide relevance in biology, chemistry and medicine. Here we show how MHz repetition rates at X-ray free-electron lasers can be used to produce microsecond time-series of protein scattering with exceptionally low noise levels of 0.001%. We demonstrate the approach by examining Jɑ helix unfolding of a light-oxygen-voltage photosensory domain. This time-resolved acquisition strategy is easy to implement and widely applicable for direct observation of structural dynamics of many biochemical processes.
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- PAR ID:
- 10521165
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Nature Methods
- Volume:
- 21
- Issue:
- 9
- ISSN:
- 1548-7091
- Format(s):
- Medium: X Size: p. 1608-1611
- Size(s):
- p. 1608-1611
- Sponsoring Org:
- National Science Foundation
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