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Big data needs approximate computing: technical perspective

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Communications of the ACM, Volume 58, Issue 1

Page 104

https://doi.org/10.1145/2688072

Published: 23 December 2014 Publication History

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Chen YZhang RKan YYang SNakashima Y(2024)Bisection Neural Network Toward Reconfigurable Hardware ImplementationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.319582135:3(3663-3673)Online publication date: Mar-2024
https://doi.org/10.1109/TNNLS.2022.3195821
Choi J(2024)A Study on Approximate Computing for Non-volatile Memory-Based Memory SystemsJournal of Electrical Engineering & Technology10.1007/s42835-024-01795-xOnline publication date: 29-Jan-2024
https://doi.org/10.1007/s42835-024-01795-x
Hanif MHafiz RShafique M(2023)DAEM: A Data- and Application-Aware Error Analysis Methodology for Approximate AddersInformation10.3390/info1410057014:10(570)Online publication date: 17-Oct-2023
https://doi.org/10.3390/info14100570
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Big data needs approximate computing: technical perspective

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Published In

Communications of the ACM Volume 58, Issue 1

January 2015

105 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/2688498

Editor:
Moshe Y. Vardi
Association for Computing Machinery, New York, NY

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Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 23 December 2014

Published in CACM Volume 58, Issue 1

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Chen YZhang RKan YYang SNakashima Y(2024)Bisection Neural Network Toward Reconfigurable Hardware ImplementationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.319582135:3(3663-3673)Online publication date: Mar-2024
https://doi.org/10.1109/TNNLS.2022.3195821
Choi J(2024)A Study on Approximate Computing for Non-volatile Memory-Based Memory SystemsJournal of Electrical Engineering & Technology10.1007/s42835-024-01795-xOnline publication date: 29-Jan-2024
https://doi.org/10.1007/s42835-024-01795-x
Hanif MHafiz RShafique M(2023)DAEM: A Data- and Application-Aware Error Analysis Methodology for Approximate AddersInformation10.3390/info1410057014:10(570)Online publication date: 17-Oct-2023
https://doi.org/10.3390/info14100570
Mahmud MHuang JRuby RWu K(2023)An ensemble method for estimating the number of clusters in a big data set using multiple random samplesJournal of Big Data10.1186/s40537-023-00709-410:1Online publication date: 1-Apr-2023
https://doi.org/10.1186/s40537-023-00709-4
Masadeh MHasan OTahar S(2023)Adaptive Approximate Accelerators with Controlled Quality Using Machine LearningDesign and Applications of Emerging Computer Systems10.1007/978-3-031-42478-6_19(501-529)Online publication date: 17-Aug-2023
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Barua HMondal K(2023)Cloud Big Data Mining and Analytics: Bringing Greenness and Acceleration in the CloudMachine Learning for Data Science Handbook10.1007/978-3-031-24628-9_22(491-510)Online publication date: 26-Feb-2023
https://doi.org/10.1007/978-3-031-24628-9_22
Balasubramanian PNayar RMaskell D(2022)Digital Image Blending Using Inaccurate AdditionElectronics10.3390/electronics1119309511:19(3095)Online publication date: 28-Sep-2022
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Balasubramanian PNayar RMin OMaskell D(2022)Digital Image Blending by Inexact MultiplicationElectronics10.3390/electronics1118286811:18(2868)Online publication date: 10-Sep-2022
https://doi.org/10.3390/electronics11182868
Balasubramanian PNayar RMaskell D(2022)Digital Image Compression Using Approximate AdditionElectronics10.3390/electronics1109136111:9(1361)Online publication date: 25-Apr-2022
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Balasubramanian PNayar RMin OMaskell D(2022)Approximator: A Software Tool for Automatic Generation of Approximate Arithmetic CircuitsComputers10.3390/computers1101001111:1(11)Online publication date: 8-Jan-2022
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Big Data Analytics

The anatomy of big data computing

Next-Generation Big Data: A Practical Guide to Apache Kudu, Impala, and Spark

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