Recurrent Neural Network-Assisted Adaptive Sampling for Approximate Computing
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Feng, Y; Zhou, Y; Tarokh, V
Published in: Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
December 1, 2019
We propose an adaptive signal sampling approach that dynamically adjusts the sampling rate to approximate the local Nyquist rate of the signal. The proposed adaptive sampling approach consists of a recurrent neural network-based change detector that detects the point of frequency change and a local Nyquist rate estimator based on a multi-rate signal processing scheme. We empirically demonstrate that our adaptive sampling approach significantly reduces the overall sampling rate for various types of signals and therefore improves the computational efficiency of subsequent signal processing.
Duke Scholars
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Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
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Publication Date
December 1, 2019
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2240 / 2246
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Feng, Y., Zhou, Y., & Tarokh, V. (2019). Recurrent Neural Network-Assisted Adaptive Sampling for Approximate Computing. In Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 2240–2246). https://doi.org/10.1109/BigData47090.2019.9006504
Feng, Y., Y. Zhou, and V. Tarokh. “Recurrent Neural Network-Assisted Adaptive Sampling for Approximate Computing.” In Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, 2240–46, 2019. https://doi.org/10.1109/BigData47090.2019.9006504.
Feng Y, Zhou Y, Tarokh V. Recurrent Neural Network-Assisted Adaptive Sampling for Approximate Computing. In: Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. 2019. p. 2240–6.
Feng, Y., et al. “Recurrent Neural Network-Assisted Adaptive Sampling for Approximate Computing.” Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, 2019, pp. 2240–46. Scopus, doi:10.1109/BigData47090.2019.9006504.
Feng Y, Zhou Y, Tarokh V. Recurrent Neural Network-Assisted Adaptive Sampling for Approximate Computing. Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. 2019. p. 2240–2246.
Published In
Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
DOI
Publication Date
December 1, 2019
Start / End Page
2240 / 2246