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• W2991367356 abstract "In this paper, a new architecture of an accelerator of a convolutional neural network is proposed. The suggested solution is pipelined and it reduces the band passing memory through the exploitation of sliding window images. Moreover, it is reconfigurable online at a convolution stride level. This proposal operates at a frequency equivalent to 280 mhz and offers a performance of 3.36 GMAC. The energy consumption is 475mw." @default.
• W2991367356 created "2019-12-05" @default.
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• W2991367356 date "2019-04-01" @default.
• W2991367356 modified "2023-09-26" @default.
• W2991367356 title "A Pipelined Energy-efficient Hardware Accelaration for Deep Convolutional Neural Networks" @default.
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• W2991367356 doi "https://doi.org/10.1109/dtss.2019.8915295" @default.
• W2991367356 hasPublicationYear "2019" @default.
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