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Low Power Dendritic Computation for Wordspotting
Georgia Institute of Technology, Atlanta 30363, GA, USA
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Received: 6 February 2013; in revised form: 7 April 2013 / Accepted: 19 April 2013 / Published: 21 May 2013
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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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.
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.
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.