Publicação
Pruning weightless neural networks
| Resumo: | Weightless neural networks (WNNs) are a type of machine learning model which perform prediction using lookup tables (LUTs) instead of arithmetic operations. Recent advancements in WNNs have reduced model sizes and improved accuracies, reducing the gap in accuracy with deep neural networks (DNNs). Modern DNNs leverage “pruning” techniques to reduce model size, but this has not previously been explored for WNNs. We propose a WNN pruning strategy based on identifying and culling the LUTs which contribute least to overall model accuracy. We demonstrate an average 40% reduction in model size with at most 1% reduction in accuracy. |
|---|---|
| Autores principais: | Susskind, Z. |
| Outros Autores: | Bacellar, A. T. L.; Arora, A.; Villon, L. A. Q.; Mendanha, R.; Araújo, L. S. de.; Dutra, D. L. C.; Lima, P. M. V.; França, F. M. G.; Miranda, I. D. S.; Breternitz Jr., M.; John, L. K. |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | documento de conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | ISCTE |
| Idioma: | inglês |
| Origem: | Repositório ISCTE |
| _version_ | 1868443496203943936 |
|---|---|
| author | Susskind, Z. |
| author2 | Bacellar, A. T. L. Arora, A. Villon, L. A. Q. Mendanha, R. Araújo, L. S. de. Dutra, D. L. C. Lima, P. M. V. França, F. M. G. Miranda, I. D. S. Breternitz Jr., M. John, L. K. |
| author2_role | author author author author author author author author author author author |
| author_facet | Susskind, Z. Bacellar, A. T. L. Arora, A. Villon, L. A. Q. Mendanha, R. Araújo, L. S. de. Dutra, D. L. C. Lima, P. M. V. França, F. M. G. Miranda, I. D. S. Breternitz Jr., M. John, L. K. |
| author_role | author |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Susskind, Z.\"},{\"Person.name\":\"Bacellar, A. T. L.\"},{\"Person.name\":\"Arora, A.\"},{\"Person.name\":\"Villon, L. A. Q.\"},{\"Person.name\":\"Mendanha, R.\"},{\"Person.name\":\"Araújo, L. S. de.\"},{\"Person.name\":\"Dutra, D. L. C.\"},{\"Person.name\":\"Lima, P. M. V.\"},{\"Person.name\":\"França, F. M. G.\"},{\"Person.name\":\"Miranda, I. D. S.\"},{\"Person.name\":\"Breternitz Jr., M.\"},{\"Person.name\":\"John, L. K.\"}] |
| datacite.creators.creator.creatorName.fl_str_mv | Susskind, Z. Bacellar, A. T. L. Arora, A. Villon, L. A. Q. Mendanha, R. Araújo, L. S. de. Dutra, D. L. C. Lima, P. M. V. França, F. M. G. Miranda, I. D. S. Breternitz Jr., M. John, L. K. |
| datacite.date.Accepted.fl_str_mv | 2022-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2022-12-05T12:31:40Z |
| datacite.date.embargoed.fl_str_mv | 2022-12-05T12:31:40Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.titles.title.fl_str_mv | Pruning weightless neural networks |
| dc.creator.none.fl_str_mv | Susskind, Z. Bacellar, A. T. L. Arora, A. Villon, L. A. Q. Mendanha, R. Araújo, L. S. de. Dutra, D. L. C. Lima, P. M. V. França, F. M. G. Miranda, I. D. S. Breternitz Jr., M. John, L. K. |
| dc.date.Accepted.fl_str_mv | 2022-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2022-12-05T12:31:40Z |
| dc.date.embargoed.fl_str_mv | 2022-12-05T12:31:40Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10071/26522 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | ESANN |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.title.fl_str_mv | Pruning weightless neural networks |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_c94f |
| description | Weightless neural networks (WNNs) are a type of machine learning model which perform prediction using lookup tables (LUTs) instead of arithmetic operations. Recent advancements in WNNs have reduced model sizes and improved accuracies, reducing the gap in accuracy with deep neural networks (DNNs). Modern DNNs leverage “pruning” techniques to reduce model size, but this has not previously been explored for WNNs. We propose a WNN pruning strategy based on identifying and culling the LUTs which contribute least to overall model accuracy. We demonstrate an average 40% reduction in model size with at most 1% reduction in accuracy. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | iscte_38bcea3f3dee55bedd5de024b3d41a4e |
| identifier.url.fl_str_mv | http://hdl.handle.net/10071/26522 |
| instacron_str | iscte |
| institution | ISCTE |
| instname_str | ISCTE |
| language | eng |
| network_acronym_str | iscte |
| network_name_str | Repositório ISCTE |
| oai_identifier_str | oai:repositorio.iscte-iul.pt:10071/26522 |
| organization_str_mv | urn:organizationAcronym:iscte |
| person_str_mv | Susskind, Z. Bacellar, A. T. L. Arora, A. Villon, L. A. Q. Mendanha, R. Araújo, L. S. de. Dutra, D. L. C. Lima, P. M. V. França, F. M. G. Miranda, I. D. S. Breternitz Jr., M. John, L. K. |
| publishDate | 2022 |
| publisher.none.fl_str_mv | ESANN |
| reponame_str | Repositório ISCTE |
| repository_id_str | urn:repositoryAcronym:iscte |
| service_str_mv | urn:repositoryAcronym:iscte |
| spelling | engWeightless neural networks (WNNs) are a type of machine learning model which perform prediction using lookup tables (LUTs) instead of arithmetic operations. Recent advancements in WNNs have reduced model sizes and improved accuracies, reducing the gap in accuracy with deep neural networks (DNNs). Modern DNNs leverage “pruning” techniques to reduce model size, but this has not previously been explored for WNNs. We propose a WNN pruning strategy based on identifying and culling the LUTs which contribute least to overall model accuracy. We demonstrate an average 40% reduction in model size with at most 1% reduction in accuracy.application/pdfengESANNengPruning weightless neural networksSusskind, Z.Bacellar, A. T. L.Arora, A.Villon, L. A. Q.Mendanha, R.Araújo, L. S. de.Dutra, D. L. C.Lima, P. M. V.França, F. M. G.Miranda, I. D. S.Breternitz Jr., M.John, L. K.Handlehttp://hdl.handle.net/10071/26522ISBNIsPartOf978287587084-1DOIIsPartOf10.14428/esann/2022.ES2022-552022-12-05T12:31:40Z2022-01-01T00:00:00Z20222022-12-05T12:27:28Zhttp://purl.org/coar/access_right/c_abf2open access1475404 byteshttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://repositorio.iscte-iul.pt/bitstreams/c87ac2c2-9bde-48ed-9658-8729b336ca96/downloadother research producthttp://purl.org/coar/resource_type/c_c94fconference object |
| spellingShingle | Pruning weightless neural networks Susskind, Z. |
| status | SINGLETON |
| title | Pruning weightless neural networks |
| title_full | Pruning weightless neural networks |
| title_fullStr | Pruning weightless neural networks |
| title_full_unstemmed | Pruning weightless neural networks |
| title_short | Pruning weightless neural networks |
| title_sort | Pruning weightless neural networks |
| url | http://hdl.handle.net/10071/26522 |
| visible | 1 |