Binary Quantization Analysis of Neural Networks Weights on MNIST Dataset
Binary Quantization Analysis of Neural Networks Weights on MNIST Dataset
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Kaunas University of Technology, Faculty of Telecommunications and Electronics
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Language
English
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Kaunas University of Technology, Faculty of Telecommunications and Electronics
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This paper considers the design of a binary scalar quantizer of Laplacian source and its application in compressed neural networks. The quantizer performance is investigated in a wide dynamic range of data variances, and for that purpose, we derive novel closed-form expressions. Moreover, we propose two selection criteria for the variance range of...
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Full title
Binary Quantization Analysis of Neural Networks Weights on MNIST Dataset
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TN_cdi_doaj_primary_oai_doaj_org_article_585879c746ae445e99860eea5251e2b0
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_585879c746ae445e99860eea5251e2b0
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ISSN
1392-1215
E-ISSN
2029-5731
DOI
10.5755/j02.eie.28881
