Next Article in Journal
Low-Power and Optimized VLSI Implementation of Compact Recursive Discrete Fourier Transform (RDFT) Processor for the Computations of DFT and Inverse Modified Cosine Transform (IMDCT) in a Digital Radio Mondiale (DRM) and DRM+ Receiver
Next Article in Special Issue
A Digital Auto-Zeroing Circuit to Reduce Offset in Sub-Threshold Sense Amplifiers
Previous Article in Journal / Special Issue
Exploration of Sub-VT and Near-VT 2T Gain-Cell Memories for Ultra-Low Power Applications under Technology Scaling
J. Low Power Electron. Appl. 2013, 3(2), 73-98; doi:10.3390/jlpea3020073

Low Power Dendritic Computation for Wordspotting

* ,
Georgia Institute of Technology, Atlanta 30363, GA, USA
* Author to whom correspondence should be addressed.
Received: 6 February 2013 / Revised: 7 April 2013 / Accepted: 19 April 2013 / Published: 21 May 2013
(This article belongs to the Special Issue Selected Papers from SubVt 2012 Conference)
Download PDF [1436 KB, uploaded 21 May 2013]


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 computational modeling; hidden markov models; neuromorphic; dendrites
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.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
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.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here


Cited By

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