Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/26548
Registo completo
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Villon, L. A. Q. | - |
dc.contributor.author | Susskind, Z. | - |
dc.contributor.author | Bacellar, A. T. L. | - |
dc.contributor.author | Miranda, I. D. S. | - |
dc.contributor.author | Araújo, L. S. de. | - |
dc.contributor.author | Lima, P. M. V. | - |
dc.contributor.author | Breternitz Jr, M. | - |
dc.contributor.author | John, L. K. | - |
dc.contributor.author | França, F. M. G. | - |
dc.contributor.author | Dutra, D. L. C. | - |
dc.date.accessioned | 2022-12-06T10:50:26Z | - |
dc.date.available | 2022-12-06T10:50:26Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Villon, L. A. Q., Susskind, Z., Bacellar, A. T. L., Miranda, I. D. S., Araújo, L. S. de., Lima, P. M. V., Breternitz Jr., M., John, L. K., França, F. M. G., & Dutra, D. L. C. (2022). A WiSARD-based conditional branch predictor. In ESANN 2022 proceedings (pp. 25-30). https://doi.org/10.14428/esann/2022.ES2022-65 | - |
dc.identifier.isbn | 978287587 084-1 | - |
dc.identifier.uri | http://hdl.handle.net/10071/26548 | - |
dc.description.abstract | Conditional branch prediction is a technique used to speculatively execute instructions before knowing the direction of conditional branch statements. Perceptron-based predictors have been extensively studied, however, they need large input sizes for the data to be linearly separable. To learn nonlinear functions from the inputs, we propose a conditional branch predictor based on the WiSARD model and compare it with two state-of-the-art predictors, the TAGE-SC-L and the Multiperspective Perceptron. We show that the WiSARD-based predictor with a smaller input size outperforms the perceptron-based predictor by about 0.09% and achieves similar accuracy to that of TAGE-SC-L. | eng |
dc.language.iso | eng | - |
dc.publisher | ESANN | - |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04466%2F2020/PT | - |
dc.relation | POCI-01-0247-FEDER-045912 | - |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04466%2F2020/PT | - |
dc.relation.ispartof | ESANN 2022 proceedings | - |
dc.rights | openAccess | - |
dc.title | A WiSARD-based conditional branch predictor | eng |
dc.type | conferenceObject | - |
dc.event.title | 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning | - |
dc.event.type | Conferência | pt |
dc.event.location | Bruges (online) | eng |
dc.event.date | 2022 | - |
dc.pagination | 25 - 30 | - |
dc.peerreviewed | yes | - |
dc.date.updated | 2022-12-06T10:48:00Z | - |
dc.description.version | info:eu-repo/semantics/publishedVersion | - |
dc.identifier.doi | 10.14428/esann/2022.ES2022-65 | - |
dc.subject.fos | Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | por |
iscte.identifier.ciencia | https://ciencia.iscte-iul.pt/id/ci-pub-91147 | - |
Aparece nas coleções: | ISTAR-CRI - Comunicações a conferências internacionais |
Ficheiros deste registo:
Ficheiro | Tamanho | Formato | |
---|---|---|---|
conferenceobject_91147.pdf | 3,27 MB | Adobe PDF | Ver/Abrir |
Todos os registos no repositório estão protegidos por leis de copyright, com todos os direitos reservados.