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- W2912130182 abstract "In the next era of distributed computing, brain-based computers that perform operations locally rather than in remote servers would be a major benefit in reducing global energy costs. A new generation of emerging nonvolatile memory devices is a leading candidate for achieving this neuromorphic vision. Using theoretical and experimental work, we have explored critical issues that arise when physically realizing modern artificial neural network (ANN) architectures using emerging memory devices (“memristors”). In our experimental work, we showed organic nanosynapses adapting automatically as logic gates via a companion digital neuron and programmable logic cell (FGPA). In our theoretical work, we also considered multilayer memristive ANNs. We have developed and simulated random projection (NoProp) and backpropagation (Multilayer Perceptron) variants that use two crossbars. These local learning systems showed critical dependencies on the physical constraints of nanodevices. Finally, we examined how feed-forward ANN designs can be modified to exploit temporal effects. We focused in particular on improving bio-inspiration and performance of the NoProp system, for example, we improved the performance with plasticity effects in the first layer. These effects were obtained using a silver ionic nanodevice with intrinsic plasticity transition behavior." @default.
- W2912130182 created "2019-02-21" @default.
- W2912130182 creator A5088323080 @default.
- W2912130182 date "2018-02-08" @default.
- W2912130182 modified "2023-09-23" @default.
- W2912130182 title "Local learning with highly analog memory devices" @default.
- W2912130182 hasPublicationYear "2018" @default.
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