To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inferen...
To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference
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Author / Creator
Qin, Qing , Ren, Jie , Yu, Jialong , Gao, Ling , Wang, Hai , Zheng, Jie , Feng, Yansong , Fang, Jianbin and Wang, Zheng
Publisher
Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Contents
The recent advances in deep neural networks (DNNs) make them attractive for embedded systems. However, it can take a long time for DNNs to make an inference on resource-constrained computing devices. Model compression techniques can address the computation issue of deep inference on embedded devices. This technique is highly attractive, as it does...
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To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference
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TN_cdi_proquest_journals_2124349933
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2124349933
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E-ISSN
2331-8422
