CrossNets: Neuromorphic Networks for Nanoelectronic Implementation
نویسندگان
چکیده
Hybrid “CMOL” integrated circuits, incorporating advanced CMOS devices for neural cell bodies, nanowires as axons and dendrites, and singlemolecule latching switches as synapses, may be used for the hardware implementation of extremely dense (~10 cells and ~10 synapses per cm) neuromorphic networks, operating up to 10 times faster than their biological prototypes. We are exploring several “CrossNet” architectures that accommodate the limitations imposed by CMOL hardware and should allow effective training of the networks without a direct external access to individual synapses. CrossNet training in the Hopfield mode have been confirmed on a software model of the network.
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