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- W2898013663 abstract "Accurate and efficient synaptic weight programming and vector-matrix multiplication are demonstrated using compound synapses constructed with ultralow power binary memristive devices having oxidized atomically thin two-dimensional hexagonal boron nitride (BNOx) filament formation layers. Experimental data of the resistive-switching current-voltage characteristics of BNOx memristors are used to formulate variation-aware models that enable statistically analyzing the trade-off between efficiency and accuracy as a function of the synaptic resolution (i.e., levels of synaptic weight programming). Results are compared with commonly reported oxide-based memristors indicating orders of magnitude (i.e., ∼105) improvements in power efficiency and ∼2-5× improvements in accuracy." @default.
- W2898013663 created "2018-10-26" @default.
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- W2898013663 date "2018-10-16" @default.
- W2898013663 modified "2023-10-10" @default.
- W2898013663 title "Efficient learning and crossbar operations with atomically-thin 2-D material compound synapses" @default.
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- W2898013663 doi "https://doi.org/10.1063/1.5042468" @default.
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