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- W4200182016 abstract "Deep learning with edge computing arises as a popular paradigm for powering edge devices with intelligence. As the size of deep neural networks (DNN) continually increases, model quantization, which converts the full-precision model into lower-bit representation while mostly preserving the accuracy, becomes a prerequisite for deploying a well-trained DNN on resource-limited edge devices. However, to properly quantize a DNN requires an essential amount of expert knowledge, or otherwise the model accuracy would be devastatingly affected. Alternatively, recent years witness the birth of third-party model supply chains which provide pretrained quantized neural networks (QNN) for free downloading." @default.
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- W4200182016 date "2021-12-06" @default.
- W4200182016 modified "2023-09-30" @default.
- W4200182016 title "Understanding the Threats of Trojaned Quantized Neural Network in Model Supply Chains" @default.
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- W4200182016 doi "https://doi.org/10.1145/3485832.3485881" @default.
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