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A low-power, high-performance speech recognition accelerator

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10.1109/TC.2019.2937075
 
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hdl:2117/175332

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Yazdani, Reza
Arnau Montañés, José MaríaMés informacióMés informació
González Colás, Antonio MaríaMés informacióMés informacióMés informació
Document typeArticle
Defense date2019-12-01
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
ProjectCoCoUnit - CoCoUnit: An Energy-Efficient Processing Unit for Cognitive Computing (EC-H2020-833057)
Abstract
Automatic Speech Recognition (ASR) is becoming increasingly ubiquitous, especially in the mobile segment. Fast and accurate ASR comes at high energy cost, not being affordable for the tiny power-budgeted mobile devices. Hardware acceleration reduces energy-consumption of ASR systems, while delivering high-performance. In this paper, we present an accelerator for largevocabulary, speaker-independent, continuous speech-recognition. It focuses on the Viterbi search algorithm representing the main bottleneck in an ASR system. The proposed design consists of innovative techniques to improve the memory subsystem, since memory is the main bottleneck for performance and power in these accelerators' design. It includes a prefetching scheme tailored to the needs of ASR systems that hides main memory latency for a large fraction of the memory accesses, negligibly impacting area. Additionally, we introduce a novel bandwidth-saving technique that removes off-chip memory accesses by 20 percent. Finally, we present a power saving technique that significantly reduces the leakage power of the accelerators scratchpad memories, providing between 8.5 and 29.2 percent reduction in entire power dissipation. Overall, the proposed design outperforms implementations running on the CPU by orders of magnitude, and achieves speedups between 1.7x and 5.9x for different speech decoders over a highly optimized CUDA implementation running on Geforce-GTX-980 GPU, while reducing the energy by 123-454x.
Description
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
CitationYazdani, R.; Arnau, J.; Gonzalez, A. A low-power, high-performance speech recognition accelerator. "IEEE transactions on computers", 1 Desembre 2019, vol. 68, núm. 12, p. 1817-1831. 
URIhttp://hdl.handle.net/2117/175332
DOI10.1109/TC.2019.2937075
ISSN0018-9340
Publisher versionhttps://ieeexplore.ieee.org/document/8812893
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  • Departament d'Arquitectura de Computadors - Articles de revista [910]
  • ARCO - Microarquitectura i Compiladors - Articles de revista [60]
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