Publication:

Fragmented Huffman-Based Compression Methodology for CNN Targeting Resource-Constrained Edge Devices

Date

2022
Journal article
https://doi.org/10.1007/s00034-022-01968-x
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dc.contributor.authorPal, Chandrajit
dc.contributor.authorPankaj, Sunil
dc.contributor.authorAkram, Wasim
dc.contributor.authorBiswas, Dwaipayan
dc.contributor.authorMattela, Govardhan
dc.contributor.authorAcharyya, Amit
dc.contributor.imecauthorBiswas, Dwaipayan
dc.contributor.orcidextPankaj, Sunil::0000-0002-8264-5608
dc.contributor.orcidextAkram, Wasim::0000-0003-3451-9305
dc.contributor.orcidextAcharyya, Amit::0000-0002-5636-0676
dc.contributor.orcidimecBiswas, Dwaipayan::0000-0002-1087-3433
dc.date.accessioned2023-08-11T07:58:45Z
dc.date.available2023-06-20T10:35:58Z
dc.date.available2023-08-11T07:58:45Z
dc.date.embargo9999-12-31
dc.date.issued2022
dc.description.wosFundingTextAuthors would like to acknowledge the support extended by the Defence Research and Development Organization, Ministry of Defence, Government of India with the Grant reference: ERIPR/ER/202009001/M/01/1781 dated 8 February 2021 for the research project entitled "Reconfigurable Machine Learning Accelerator Design and Development for Avionics Applications." Authors would also like to acknowledge the support received by the Ministry of the Electronics and Information Technology (MEITY), Government of India toward the usage of the CAD tools as part of the Special Manpower Development (SMDP) program. The authors would also like to thank Ceremorphic Technologies Private Limited for funding and extending the tool support for carrying out few experiments.
dc.identifier.doi10.1007/s00034-022-01968-x
dc.identifier.issn0278-081X
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/41924
dc.publisherSPRINGER BIRKHAUSER
dc.source.beginpage3957
dc.source.endpage3984
dc.source.issue7
dc.source.journalCIRCUITS SYSTEMS AND SIGNAL PROCESSING
dc.source.numberofpages28
dc.source.volume41
dc.subject.keywordsNEURAL-NETWORK ACCELERATOR
dc.subject.keywordsMEMORY
dc.subject.keywordsPROCESSOR
dc.title

Fragmented Huffman-Based Compression Methodology for CNN Targeting Resource-Constrained Edge Devices

dc.typeJournal article
dspace.entity.typePublication
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