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• W2090413197 abstract "A Cellular Neural Network (CNN) is a highly-parallel, analog processor that can significantly outperform von Neumann architectures for certain classes of problems. Here, we show how emerging, beyond-CMOS devices could help to further enhance the capabilities of CNNs, particularly for solving problems with non-binary outputs. We show how CNNs based on devices such as graphene transistors -- with multiple steep current growth regions separated by negative differential resistance (NDR) in their I-V characteristics -- could be used to recognize multiple patterns simultaneously. (This would require multiple steps given a conventional, binary CNN.) Also, we demonstrate how tunneling field effect transistors (TFETs) can be used to form circuits capable of performing similar tasks. With this approach, more exotic device I-V characteristics are not required -- which should be an asset when considering issues such as cell-to-cell mismatch, etc. As a case study, we present a CNN-cell design that employs TFET-based circuitry to realize ternary outputs. We then illustrate how this hardware could be employed to efficiently solve a tactile sensing problem. The total number of computation steps as well as the required hardware could be reduced significantly when compared to an approach based on a conventional CNN." @default.
• W2090413197 created "2016-06-24" @default.
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• W2090413197 date "2014-03-24" @default.
• W2090413197 modified "2023-09-22" @default.
• W2090413197 title "Impact of steep-slope transistors on non-von neumann architectures: CNN case study" @default.
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• W2090413197 doi "https://doi.org/10.5555/2616606.2616775" @default.
• W2090413197 hasPublicationYear "2014" @default.
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