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IJCNN
2006
IEEE

In Situ Training of CMOL CrossNets

14 years 5 months ago
In Situ Training of CMOL CrossNets
—— Hybrid semiconductor/nanodevice (“CMOL”) technology may allow the implementation of digital and mixed-signal integrated circuits, including artificial neural networks (“CrossNets”), with unparalleled density and speed. However, previously suggested methods of CrossNet training may be impracticable for large-scale applications of these networks. In this work, we are describing two new methods of “in situ” training of CrossNets, based on either genuinely stochastic or pseudo-stochastic multiplication of analog signals, which may be readily implemented in CMOL circuits. The methods have been tested by numerical simulation of CrossNet-based perceptrons by error backpropagation on three problems of the Proben1 benchmark dataset. The testing gave very encouraging results: CMOL CrossNets with their binary elementary synapses may provide, after the in situ training, classification performance at least on a par with the best results reported for software-based networks with c...
Jung Hoon Lee, Konstantin Likharev
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where IJCNN
Authors Jung Hoon Lee, Konstantin Likharev
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