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- W4386323366 abstract "Quantization is one of the optimization methods for developing deep learning models for edge devices. Through converting the floating-point into 8bit integer or even lower bitwidth, the model’s storage size can be reduced. As the rounding error exists during the quantization process, the model performance decreases. As a result, a method that can recover model performance is needed. In this research, a compensation method for improving the performance of quantized deep learning models is proposed, which make the quantized model can achieve equal or even better performance compared to the original floating-point model." @default.
- W4386323366 created "2023-09-01" @default.
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- W4386323366 date "2023-07-17" @default.
- W4386323366 modified "2023-09-28" @default.
- W4386323366 title "Compensation Method of Quantized Deep Learning Models for Edge Devices" @default.
- W4386323366 doi "https://doi.org/10.1109/icce-taiwan58799.2023.10226977" @default.
- W4386323366 hasPublicationYear "2023" @default.
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