Journal of Low Power Electronics and Applications | |
Low Power Dendritic Computation for Wordspotting | |
Suma George1  Jennifer Hasler2  Scott Koziol2  Stephen Nease2  | |
[1] Georgia Institute of Technology, Atlanta 30363, GA, USA; | |
关键词: computational modeling; hidden markov models; neuromorphic; dendrites; | |
DOI : 10.3390/jlpea3020073 | |
来源: mdpi | |
【 摘 要 】
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/
【 授权许可】
CC BY
© 2013 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
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RO202003190036233ZK.pdf | 1435KB | download |