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This paper presents novel reconfigurable architectures for reducing the latency of recurrent neural networks (RNNs) that are used for detecting gravitational waves.
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E. Azari and S. Vrudhula, “An energy-efficient reconfigurable lstm accelerator for natural language processing,” in
2019
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“Generate Gravitational-Wave Data (GGWD),”
2019
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Z. Que, H. Nakahara, E. Nurvitadhi, H. Fan, C. Zeng, J. Meng, X. Niu, and W. Luk, “Optimizing Reconfigurable Recurrent Neural Networks,” in
2020
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H. Nakahara
2020
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V. Rybalkin and N. Wehn, “When Massive GPU Parallelism Ain’t Enough: A Novel Hardware Architecture of 2D-LSTM Neural Network,” in
2020
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Y.-C. Lin and J.-H. P. Wu, “Detection of gravitational waves using Bayesian neural networks,”
2021
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D. Thorwarth and D. A. Low, “Technical Challenges of Real-Time Adaptive MR-Guided Radiotherapy,”
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M. Langhammer
2021
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