Machine Learning and Manycore Systems Design: A Serendipitous Symbiosis.
Tight collaboration between manycore system designers and machine-learning experts is necessary to create a data-driven manycore design framework that integrates both learning and expert knowledge. Such a framework will be necessary to address the rising complexity of designing large-scale manycore...
| Publicado en: | Computer (00189162) Vol. 51; no. 7; pp. 66 - 78 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
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IEEE
Jul2018
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=130980868&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 130980868 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00189162 PUT jtl: Computer (00189162) issn: 00189162 maglogo: N pubinfo: dt: Jul2018 vid: 51 iid: 7 pid: 13605 pub: IEEE artinfo: ui: 130980868 10.1109/MC.2018.3011040 ppf: 66 ppct: 12 formats: tig: atl: Machine Learning and Manycore Systems Design: A Serendipitous Symbiosis. aug: au: Kim, Ryan Gary Doppa, Janardhan Rao Pande, Partha Pratim Marculescu, Diana Marculescu, Radu su: Machine learning Microprocessor design & construction Systems design Computational complexity Graphics processing units sug: subj: Machine learning Microprocessor design & construction Systems design Computational complexity Graphics processing units keyword: AI Decision making machine learning manycore manycore system design Optimization Runtime scientific computing Task analysis ab: Tight collaboration between manycore system designers and machine-learning experts is necessary to create a data-driven manycore design framework that integrates both learning and expert knowledge. Such a framework will be necessary to address the rising complexity of designing large-scale manycore systems and machine-learning techniques. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2018 holdings: @attributes: islocal: N |
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