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SPEED: Open-Source Framework to Accelerate Speech Recognition on Embedded GPUs
KTH, School of Information and Communication Technology (ICT), Electronics.
KTH, School of Information and Communication Technology (ICT), Electronics.ORCID iD: 0000-0003-0565-9376
KTH.
2017 (English)In: Proceedings - 20th Euromicro Conference on Digital System Design, DSD 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 94-101, article id 8049772Conference paper, Published paper (Refereed)
Abstract [en]

Due to high accuracy, inherent redundancy, and embarrassingly parallel nature, the neural networks are fast becoming mainstream machine learning algorithms. However, these advantages come at the cost of high memory and processing requirements (that can be met by either GPUs, FPGAs or ASICs). For embedded systems, the requirements are particularly challenging because of stiff power and timing budgets. Due to the availability of efficient mapping tools, GPUs are an appealing platforms to implement the neural networks. While, there is significant work that implements the image recognition (in particular Convolutional Neural Networks) on GPUs, only a few works deal with efficiently implement of speech recognition on GPUs. The work that does focus on implementing speech recognition does not address embedded systems. To tackle this issue, this paper presents SPEED (Open-source framework to accelerate speech recognition on embedded GPUs). We have used Eesen speech recognition framework because it is considered as the most accurate speech recognition technique. Experimental results reveal that the proposed techniques offer 2.6X speedup compared to state of the art.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 94-101, article id 8049772
Keywords [en]
GPU, Machine learning, Neural Networks, Speech Recognition
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:kth:diva-223018DOI: 10.1109/DSD.2017.89ISI: 000427097100013Scopus ID: 2-s2.0-85034452083ISBN: 9781538621455 OAI: oai:DiVA.org:kth-223018DiVA, id: diva2:1185546
Conference
20th Euromicro Conference on Digital System Design, DSD 2017, Vienna, Austria, 30 August 2017 through 1 September 2017
Note

QC 20180226

Available from: 2018-02-26 Created: 2018-02-26 Last updated: 2018-04-03Bibliographically approved

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Hemani, Ahmed

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf