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- W3089500695 abstract "Hardware acceleration for dilated and transposed convolution enables real time execution of related tasks like segmentation, but current designs are specific for these convolutional types or suffer from complex control for reconfigurable designs. This paper presents a design that decomposes input or weight for dilated and transposed convolutions respectively to skip redundant computations and thus executes efficiently on existing dense CNN hardware as well. The proposed architecture can cut down 87.8% of the cycle counts to achieve 8.2X speedup over a naive execution for the ENet case." @default.
- W3089500695 created "2020-10-08" @default.
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- W3089500695 date "2020-10-01" @default.
- W3089500695 modified "2023-09-24" @default.
- W3089500695 title "Efficient Accelerator for Dilated and Transposed Convolution with Decomposition" @default.
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- W3089500695 doi "https://doi.org/10.1109/iscas45731.2020.9180402" @default.
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