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• W3136300372 endingPage "102849" @default.
• W3136300372 startingPage "102849" @default.
• W3136300372 abstract "With the development of technology and society, artificial intelligence has entered the era of deep learning neural networks. Convolution operation and image processing are fundamental technologies for deep neural networks and artificial intelligence, so reducing the energy consumption of convolution operation and increasing the speed of image processing will promote the development of a sustainable intelligent society. Traditional image processing technologies are based on the Von Neumann architecture, which is slow and not convenient for the hardware implementation of neural networks. Therefore, this paper breaks through the Von Neumann architecture, and uses the forgetting memristor bridge to realize the parallel image processing on the neumorphic chips. We use the forgetting characteristics of the memristor bridge to switch operators, and design single-operator switching, double-operator switching and K-operator switching three system architectures. The image operator switching systems designed in this paper not only have the similar processing effect of the traditional way, but also have the advantages of easy control, fast running speed (the processing speed is reduced from ms to μs), and low power consumption (the power consumption is reduced by nearly half), which will bring immeasurable benefits to the sustainable intelligent society." @default.
• W3136300372 created "2021-03-29" @default.
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• W3136300372 date "2021-06-01" @default.
• W3136300372 modified "2023-10-18" @default.
• W3136300372 title "Low power convolutional architectures: Three operator switching systems based on forgetting memristor bridge" @default.
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• W3136300372 doi "https://doi.org/10.1016/j.scs.2021.102849" @default.
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