J. Low Power Electron. Appl. 2013, 3(2), 73-98; doi:10.3390/jlpea3020073

Low Power Dendritic Computation for Wordspotting

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Received: 6 February 2013; in revised form: 7 April 2013 / Accepted: 19 April 2013 / Published: 21 May 2013
(This article belongs to the Special Issue Selected Papers from SubVt 2012 Conference)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: In this paper, we demonstrate how a network of dendrites can be used to build the state decoding block of a wordspotter similar to a Hidden Markov Model (HMM) classifier structure. We present simulation and experimental data for a single line dendrite and also experimental results for a dendrite-based classifier structure. This work builds on previously demonstrated building blocks of a neural network: the channel, synapses and dendrites using CMOS circuits. These structures can be used for speech and pattern recognition. The computational efficiency of such a system is >10 MMACs/μW as compared to Digital Systems which perform 10 MMACs/mW.
Keywords: computational modeling; hidden markov models; neuromorphic; dendrites
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MDPI and ACS Style

George, S.; Hasler, J.; Koziol, S.; Nease, S.; Ramakrishnan, S. Low Power Dendritic Computation for Wordspotting. J. Low Power Electron. Appl. 2013, 3, 73-98.

AMA Style

George S, Hasler J, Koziol S, Nease S, Ramakrishnan S. Low Power Dendritic Computation for Wordspotting. Journal of Low Power Electronics and Applications. 2013; 3(2):73-98.

Chicago/Turabian Style

George, Suma; Hasler, Jennifer; Koziol, Scott; Nease, Stephen; Ramakrishnan, Shubha. 2013. "Low Power Dendritic Computation for Wordspotting." J. Low Power Electron. Appl. 3, no. 2: 73-98.

J. Low Power Electron. Appl. EISSN 2079-9268 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert