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Article

A Novel FPGA-Based Intent Recognition System Utilizing Deep Recurrent Neural Networks

1
Computer Engineering & Informatics Department, University of Patras, 26504 Patra, Greece
2
School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
*
Authors to whom correspondence should be addressed.
Academic Editor: Luis Gomes
Electronics 2021, 10(20), 2495; https://doi.org/10.3390/electronics10202495
Received: 20 September 2021 / Revised: 6 October 2021 / Accepted: 11 October 2021 / Published: 13 October 2021
In recent years, systems that monitor and control home environments, based on non-vocal and non-manual interfaces, have been introduced to improve the quality of life of people with mobility difficulties. In this work, we present the reconfigurable implementation and optimization of such a novel system that utilizes a recurrent neural network (RNN). As demonstrated in the real-world results, FPGAs have proved to be very efficient when implementing RNNs. In particular, our reconfigurable implementation is more than 150× faster than a high-end Intel Xeon CPU executing the reference inference tasks. Moreover, the proposed system achieves more than 300× the improvements, in terms of energy efficiency, when compared with the server CPU, while, in terms of the reported achieved GFLOPS/W, it outperforms even a server-tailored GPU. An additional important contribution of the work discussed in this study is that the implementation and optimization process demonstrated can also act as a reference to anyone implementing the inference tasks of RNNs in reconfigurable hardware; this is further facilitated by the fact that our C++ code, which is tailored for a high-level-synthesis (HLS) tool, is distributed in open-source, and can easily be incorporated to existing HLS libraries. View Full-Text
Keywords: intent recognition; recurrent neural network; long short-term memory; high level synthesis intent recognition; recurrent neural network; long short-term memory; high level synthesis
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MDPI and ACS Style

Tsantikidou, K.; Tampouratzis, N.; Papaefstathiou, I. A Novel FPGA-Based Intent Recognition System Utilizing Deep Recurrent Neural Networks. Electronics 2021, 10, 2495. https://doi.org/10.3390/electronics10202495

AMA Style

Tsantikidou K, Tampouratzis N, Papaefstathiou I. A Novel FPGA-Based Intent Recognition System Utilizing Deep Recurrent Neural Networks. Electronics. 2021; 10(20):2495. https://doi.org/10.3390/electronics10202495

Chicago/Turabian Style

Tsantikidou, Kyriaki, Nikolaos Tampouratzis, and Ioannis Papaefstathiou. 2021. "A Novel FPGA-Based Intent Recognition System Utilizing Deep Recurrent Neural Networks" Electronics 10, no. 20: 2495. https://doi.org/10.3390/electronics10202495

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