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- W2897568615 abstract "Edge devices often have to processdata at low power and would benefit from being adaptable. Given that the data coming into these devices is generally unlabeled, unsupervised training on these devices is beneficial. This paper examines a low power approach to implement the winner takes all algorithm, for self-organizing maps through a memristor crossbar based circuit. A novel neuron circuit is designed for the winning neuron detection and lateral inhibition operations. Our experimental results show that the proposed system can self-organize based on unlabeled training data. The proposed design was around 0.002mm$^{mathbf{2, $textbf{{in area and consumed about 0.2mW of power. When compared to a CPU, the design had a higher error rate, but was 100 times faster and consumed much lower area and power. Thus when area or power reduction are crucially important, this approach is quite viable." @default.
- W2897568615 created "2018-10-26" @default.
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- W2897568615 date "2018-07-01" @default.
- W2897568615 modified "2023-09-23" @default.
- W2897568615 title "Low Power Memristor Crossbar Based Winner Takes All Circuit" @default.
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- W2897568615 doi "https://doi.org/10.1109/ijcnn.2018.8489735" @default.
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