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- W2990865680 abstract "We present a convolutional neural network (CNN) learning processor, which accelerates the stochastic gradient descent (SGD) with a momentum-based training algorithm in 16-bit fixed-point precision. Using a new cyclic weight storage and access scheme, we use the same off-the-shelf SRAMs for nontranspose and transpose operations during feedforward (FF) and feedbackward (FB) phases, respectively, of the CNN learning process. The 65-nm CNN learning processor achieves peak energy efficiency of 2.6 TOPS/W for 16-bit fixed-point operations, consuming 10.45 mW at 0.55 V." @default.
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- W2990865680 date "2020-01-01" @default.
- W2990865680 modified "2023-09-29" @default.
- W2990865680 title "A 2.6 TOPS/W 16-Bit Fixed-Point Convolutional Neural Network Learning Processor in 65-nm CMOS" @default.
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- W2990865680 doi "https://doi.org/10.1109/lssc.2019.2954780" @default.
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