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20222026
most citedQuantune: Post-training Quantization of Convolutional Neural Networks using Extreme Gradient Boosting for Fast Deployment

21 citations · 28 across the 9 of their papers we have counts for

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cs.LG2022

CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution

Taeho Kim, Yongin Kwon, Jemin Lee +1

Mobile devices run deep learning models for various purposes, such as image classification and speech recognition. Due to the resource constraints of mobile devices, researchers ha…

cs.LG2022★ 21 cited

Quantune: Post-training Quantization of Convolutional Neural Networks using Extreme Gradient Boosting for Fast Deployment

Jemin Lee, Misun Yu, Yongin Kwon +1

To adopt convolutional neural networks (CNN) for a range of resource-constrained targets, it is necessary to compress the CNN models by performing quantization, whereby precision r…