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EncodeNet: A Framework for Boosting DNN Accuracy with Entropy-driven Generalized Converting Autoencoder
Our experimental results demonstrate that EncodeNet improves the accuracy of VGG16 from 92. 64% to 94. 05% on CIFAR-10 and RestNet20 from 74. 56% to 76. 04% on CIFAR-100.
A Converting Autoencoder Toward Low-latency and Energy-efficient DNN Inference at the Edge
We present CBNet, a low-latency and energy-efficient DNN inference framework tailored for edge devices.
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