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More is Less: Domain-Specific Speech Recognition Microprocessor Using One-Dimensional Convolutional Recurrent Neural Network.
- Source :
-
IEEE Transactions on Circuits & Systems. Part I: Regular Papers . Apr2022, Vol. 69 Issue 4, p1571-1582. 12p. - Publication Year :
- 2022
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Abstract
- Low-power keywords recognition has been a focus of acoustic signal processing for several decades. This work investigates the domain-specific speech recognition microprocessor based on optimized one-dimensional convolutional recurrent neural network (1D-CRNN). Compared to previous DNN based frameworks, the proposed 1D-CRNN can process both the feature extraction and keywords classification, and achieve high recognition accuracy with reduced computation operations under wide range background noise SNRs. An energy-efficient 1D-CRNN accelerator is implemented to dynamically reconfigure and process the different layers. This accelerator has the characteristics of “More is Less” in three aspects: 1) the hybrid network with more complex layers is much more compact and requires less computation; 2) although the weight width quantized to 8 bits requires more memory size and multiplication energy cost, the required network neurons can be reduced and hardware utilization can be improved; 3) an energy-aware self-compensation tensor multiplication unit with dual power supply based on approximation design method can be utilized for 1D-CRNN computing. Compared to the state-of-the-art architectures, the novel more-is-less architecture can achieve a much lower power consumption of $1.4~\mu \text{W}\sim 2.1~\mu \text{W}$ (over 80% reduced) under an industry 22nm technology, while maintaining higher system adaptability (support SNRs: −5dB~Clean) for 1~5 real-time keywords recognition. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15498328
- Volume :
- 69
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Circuits & Systems. Part I: Regular Papers
- Publication Type :
- Periodical
- Accession number :
- 156247778
- Full Text :
- https://doi.org/10.1109/TCSI.2021.3134271