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- W3166514834 abstract "In this article we demonstrate and compare the performance of 32nm technology node compatible high-K and low-K stacked RRAM with CFET-SRAM and MRAM for binary deep neural network. We have fabricated heterogenous stacked RRAM with Sidoped Al <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> O <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>3</sub> and Ta <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> O <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>5</sub> as stacked layer for synaptic memory application. The device demonstrated an exorbitant on/off ratio ~ 4.2 x 10 <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>3</sup> with an ultra-low variation (σ ~ 6E-07 S). We have trained the neural network with 97.11% accuracy as baseline and observed the impact of conductance variation and read noise variation. We have also benchmarked the performance of our device with CFET-SRAM and MRAM technologies from other works and observed superior performance of our devices in terms of accuracy." @default.
- W3166514834 created "2021-06-22" @default.
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- W3166514834 date "2021-04-19" @default.
- W3166514834 modified "2023-09-26" @default.
- W3166514834 title "Benchmarking the Performance of Heterogeneous Stacked RRAM with CFETSRAM and MRAM for Deep Neural Network Application Amidst Variation and Noise" @default.
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- W3166514834 doi "https://doi.org/10.1109/vlsi-tsa51926.2021.9440130" @default.
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