Tree-based machine learning performed in-memory with memristive analog CAM

Tree-based machine learning algorithms are known to be explainable and effective even trained on limited datasets, however difficult to optimize on conventional digital hardware. The authors apply analog content addressable memory to accelerate tree-based model inference for improved performance.

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Autores principales:	Giacomo Pedretti, Catherine E. Graves, Sergey Serebryakov, Ruibin Mao, Xia Sheng, Martin Foltin, Can Li, John Paul Strachan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/2918fbbcb63643d0ba6ac994f9f06593
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id	oai:doaj.org-article:2918fbbcb63643d0ba6ac994f9f06593
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spelling	oai:doaj.org-article:2918fbbcb63643d0ba6ac994f9f065932021-12-02T19:16:12ZTree-based machine learning performed in-memory with memristive analog CAM10.1038/s41467-021-25873-02041-1723https://doaj.org/article/2918fbbcb63643d0ba6ac994f9f065932021-10-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25873-0https://doaj.org/toc/2041-1723Tree-based machine learning algorithms are known to be explainable and effective even trained on limited datasets, however difficult to optimize on conventional digital hardware. The authors apply analog content addressable memory to accelerate tree-based model inference for improved performance.Giacomo PedrettiCatherine E. GravesSergey SerebryakovRuibin MaoXia ShengMartin FoltinCan LiJohn Paul StrachanNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Giacomo Pedretti Catherine E. Graves Sergey Serebryakov Ruibin Mao Xia Sheng Martin Foltin Can Li John Paul Strachan Tree-based machine learning performed in-memory with memristive analog CAM
description	Tree-based machine learning algorithms are known to be explainable and effective even trained on limited datasets, however difficult to optimize on conventional digital hardware. The authors apply analog content addressable memory to accelerate tree-based model inference for improved performance.
format	article
author	Giacomo Pedretti Catherine E. Graves Sergey Serebryakov Ruibin Mao Xia Sheng Martin Foltin Can Li John Paul Strachan
author_facet	Giacomo Pedretti Catherine E. Graves Sergey Serebryakov Ruibin Mao Xia Sheng Martin Foltin Can Li John Paul Strachan
author_sort	Giacomo Pedretti
title	Tree-based machine learning performed in-memory with memristive analog CAM
title_short	Tree-based machine learning performed in-memory with memristive analog CAM
title_full	Tree-based machine learning performed in-memory with memristive analog CAM
title_fullStr	Tree-based machine learning performed in-memory with memristive analog CAM
title_full_unstemmed	Tree-based machine learning performed in-memory with memristive analog CAM
title_sort	tree-based machine learning performed in-memory with memristive analog cam
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2918fbbcb63643d0ba6ac994f9f06593
work_keys_str_mv	AT giacomopedretti treebasedmachinelearningperformedinmemorywithmemristiveanalogcam AT catherineegraves treebasedmachinelearningperformedinmemorywithmemristiveanalogcam AT sergeyserebryakov treebasedmachinelearningperformedinmemorywithmemristiveanalogcam AT ruibinmao treebasedmachinelearningperformedinmemorywithmemristiveanalogcam AT xiasheng treebasedmachinelearningperformedinmemorywithmemristiveanalogcam AT martinfoltin treebasedmachinelearningperformedinmemorywithmemristiveanalogcam AT canli treebasedmachinelearningperformedinmemorywithmemristiveanalogcam AT johnpaulstrachan treebasedmachinelearningperformedinmemorywithmemristiveanalogcam
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Tree-based machine learning performed in-memory with memristive analog CAM

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