This presentation explores the challenges of optimizing Deep Neural Networks (DNN) for mobile vision systems due to their large size and high energy consumption. The LegoDNN framework introduces a block-grained scaling approach to reduce memory access energy consumption by compressing DNNs. The agen

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Deep learning, particularly deep neural networks (DNNs), has revolutionized machine learning with its high accuracy rates. However, in adversarial settings, adversaries can manipulate DNNs by crafting adversarial samples to force misclassification. Such attacks pose risks in various applications, in

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This study introduces an improved cepstra minimum-mean-square-error noise reduction algorithm for robust speech recognition. It explores the effectiveness of conventional noise-robust front-ends with Gaussian mixture models (GMMs) and deep neural networks (DNNs). The research demonstrates the benefi

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