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- W2896853262 abstract "Spintronic devices promise an excellent opportunity for implementing ultra-low power neuromorphic platforms due to the inherent correspondence between their physical characteristics and the required neuronal, synaptic functionalities. Neuromorphic circuits using domain wall motion-based threshold neurons have been demonstrated in previous studies. However, threshold neurons are unable to realize linearly inseparable functions. Our work addresses this challenge by proposing a new domain wall motion-based neural activation unit with XOR-like activation function. We also develop a new learning algorithm for neurons with this activation unit. Offline training is performed on real-world datasets from the UCI machine learning repository. Neuromorphic circuits corresponding to these datasets are also simulated. The results suggest femto-Joule range energy consumption of a neuron with the proposed activation unit and $ 1.08times -1.82times $ lower misclassification rate (MCR) of the proposed algorithm in comparison to the traditional perceptron learning algorithm." @default.
- W2896853262 created "2018-10-26" @default.
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- W2896853262 date "2018-07-01" @default.
- W2896853262 modified "2023-09-26" @default.
- W2896853262 title "Domain Wall Motion-based XOR-like Activation Unit With A Programmable Threshold" @default.
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- W2896853262 doi "https://doi.org/10.1109/ijcnn.2018.8489146" @default.
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