Quantization of Deep Neural Network Models Considering Per-Layer Computation Complexity for Efficient Execution in Multi-Precision Accelerators. | D2R Server publishing the DBLP Bibliography Database, hosted at L3S Research Center

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dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Shen-Fu_Hsiao>
dc:creator	<https://dblp.l3s.de/d2r/resource/authors/Yu-Che_Yen>
foaf:homepage	<http://dx.doi.org/doi.org%2F10.1109%2FICCE-TW52618.2021.9602953>
foaf:homepage	<https://doi.org/10.1109/ICCE-TW52618.2021.9602953>
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dcterms:issued	2021 (xsd:gYear)
rdfs:label	Quantization of Deep Neural Network Models Considering Per-Layer Computation Complexity for Efficient Execution in Multi-Precision Accelerators. (xsd:string)
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rdfs:seeAlso	<http://dblp.uni-trier.de/db/conf/icce-tw/icce-tw2021.html#HsiaoY21>
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dc:title	Quantization of Deep Neural Network Models Considering Per-Layer Computation Complexity for Efficient Execution in Multi-Precision Accelerators. (xsd:string)
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