NDLI: A 40-nm 168-mW 2.4×-real-time VLSI processor for 60-kWord continuous speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Guangji He Sugahara, T. Izumi, S. Kawaguchi, H. Yoshimoto, M.
Copyright Year	2012
Description	Author affiliation: Kobe University, Kobe, 657-8501 Japan (Guangji He; Sugahara, T.; Izumi, S.; Kawaguchi, H.; Yoshimoto, M.)
Abstract	This paper describes a low-power VLSI chip for speaker-independent 60-kWord continuous speech recognition based on a context-dependent Hidden Markov Model (HMM). Our implementation includes a compression-decoding scheme to reduce the external memory bandwidth for Gaussian Mixture Model (GMM) computation and multi-path Viterbi transition units. We optimize the internal SRAM size using the max-approximation GMM calculation and adjusting the number of look-ahead frames. The test chip, fabricated in 40 nm CMOS technology, occupies 1.77 mm × 2.18 mm containing 2.52 M transistors for logic and 4.29 Mbit on-chip memory. The measured results show that our implementation achieves 34.2% required frequency reduction (83.3 MHz) and reduces 48.5% power consumption (74.14 mW) for 60 k-Word real-time continuous speech recognition compared to the previous work. This chip can maximally process 2.4× faster than real-time at 200 MHz and 1.1 V with power consumption of 168 mW.
Starting Page	1
Ending Page	4
File Size	1047633
Page Count	4
File Format	PDF
ISBN	9781467315555
ISSN	08865930
e-ISBN	9781467315562
e-ISBN	9781467315548
DOI	10.1109/CICC.2012.6330678
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech recognition Random access memory Viterbi algorithm Hidden Markov models Real-time systems Very large scale integration Field programmable gate arrays large vocabulary continuous speech recognition (LVCSR) 2.4× 40 nm VLSI hidden Markov model (HMM)
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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