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• W3016048805 abstract "To support various edge applications, a neural network accelerator needs to achieve high flexibility and classification accuracy within a limited power budget. This paper proposes a weight tuning algorithm to improve the energy efficiency by lowering the switching activity. A flexible and runtime-reconfigurable CNN accelerator is co-designed with the algorithm and demonstrated with a feature extraction processor on an FPGA. The system is fully self-contained for small CNNs and speech keyword spotting is shown as an example. A fully integrated custom ASIC is also being fabricated for this system. Based on post place-and-route simulation of the ASIC, the weight tuning algorithm reduces the energy consumption of weight delivery and computation by 1.70x and 1.20x respectively with little loss in accuracy." @default.
• W3016048805 created "2020-04-17" @default.
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• W3016048805 date "2019-11-01" @default.
• W3016048805 modified "2023-09-25" @default.
• W3016048805 title "Flexible Low Power CNN Accelerator for Edge Computing with Weight Tuning" @default.
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• W3016048805 doi "https://doi.org/10.1109/a-sscc47793.2019.9056941" @default.
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