Ferianc, Martin;
Que, Zhiqiang;
Fan, Hongxiang;
Luk, Wayne;
Rodrigues, Miguel;
(2021)
Optimizing Bayesian Recurrent Neural Networks on an FPGA-based
Accelerator.
In:
Proceedings of the 2021 International Conference on Field-Programmable Technology (ICFPT).
(pp. pp. 1-10).
IEEE
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Abstract
Neural networks have demonstrated their outstanding performance in a wide range of tasks. Specifically recurrent architectures based on long-short term memory (LSTM) cells have manifested excellent capability to model time dependencies in real-world data. However, standard recurrent architectures cannot estimate their uncertainty which is essential for safety-critical applications such as in medicine. In contrast, Bayesian recurrent neural networks (RNNs) are able to provide uncertainty estimation with improved accuracy. Nonetheless, Bayesian RNNs are computationally and memory demanding, which limits their practicality despite their advantages. To address this issue, we propose an FPGA-based hardware design to accelerate Bayesian LSTM-based RNNs. To further improve the overall algorithmic-hardware performance, a co-design framework is proposed to explore the most fitting algorithmic-hardware configurations for Bayesian RNNs. We conduct extensive experiments on healthcare applications to demonstrate the improvement of our design and the effectiveness of our framework. Compared with GPU implementation, our FPGA-based design can achieve up to 10 times speedup with nearly 106 times higher energy efficiency. To the best of our knowledge, this is the first work targeting acceleration of Bayesian RNNs on FPGAs.
| Type: | Proceedings paper |
|---|---|
| Title: | Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator |
| Event: | 2021 International Conference on Field-Programmable Technology (ICFPT) |
| ISBN-13: | 978-1-6654-2010-5 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICFPT52863.2021.9609847 |
| Publisher version: | https://doi.org/10.1109/ICFPT52863.2021.9609847 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Recurrent neural networks, Bayesian inference, Field-programmable gate array, Hardware acceleration |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of Arts and Humanities UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of Arts and Humanities > Dept of Information Studies UCL > Provost and Vice Provost Offices > UCL SLASH UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10150058 |
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