Architectures for Nanoelectronic Neural Networks: New Results
نویسندگان
چکیده
Our group is developing artificial neural networks that may be implemented using hybrid semiconductor/molecular (“CMOL”) circuits. Estimates show that such networks (“CrossNets”) may eventually exceed the mammal brain in areal density, at much higher speed and acceptable power consumption. In this report, we demonstrate that CrossNets based on simple (two-terminal) molecular devices can work well in at least two modes: as Hopfield networks with high defect tolerance, as well as simple and multilayer perceptrons.
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