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CrossNets: Neuromorphic Hybrid CMOS/Nanoelectronic Networks

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Hybrid CMOS/nanoelectronic circuits, combining CMOS chips with simple nanoelectronic crossbar add-ons, may extend the exponential Moore-Law progress of microelectronics beyond the 10-nm frontier. This paper reviews the development of neuromorphic networks (“CrossNets”) based on this prospective technology. In these networks, the neural cell bodies (“somas”) are implemented in the CMOS subsystem, crossbar nanowires are used as axons and dendrites, while two-terminal crosspoint devices are used as elementary synapses. Extensive analysis and simulations have shown that such networks may perform virtually all information processing tasks demonstrated with software-implemented neural networks, with much higher performance. Estimates show that CrossNets may eventually overcome bio-cortical circuits in density, at comparable connectivity, while operating 4 to 6 orders of magnitude faster, at manageable power dissipation.

Keywords: ADAPTATION; COGNITIVE TASKS; CROSSBAR; HYBRID CIRCUITS; LATCHING SWITCHES; MEMRISTIVE DEVICES; NANOELECTRONICS; NEURAL NETWORKS; NEUROMORPHIC CIRCUITS; PLASTICITY

Document Type: Review Article

Publication date: 01 June 2011

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  • Science of Advanced Materials (SAM) is an interdisciplinary peer-reviewed journal consolidating research activities in all aspects of advanced materials in the fields of science, engineering and medicine into a single and unique reference source. SAM provides the means for materials scientists, chemists, physicists, biologists, engineers, ceramicists, metallurgists, theoreticians and technocrats to publish original research articles as reviews with author's photo and short biography, full research articles and communications of important new scientific and technological findings, encompassing the fundamental and applied research in all latest aspects of advanced materials.
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