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Neural Acceleration for General-Purpose Approximate Programs.

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Title: Neural Acceleration for General-Purpose Approximate Programs.
Authors: Esmaeilzadeh, Hadi1, Sampson, Adrian1, Ceze, Luis1, Burger, Doug2
Source: IEEE Micro. May2013, Vol. 33 Issue 3, p16-27. 12p.
Subjects: Algorithms, Computer system equipment, Central processing units, Approximation theory, Computer architecture
Abstract: This work proposes an approximate algorithmic transformation and a new class of accelerators, called neural processing units (NPUs). NPUs leverage the approximate algorithmic transformation that converts regions of code from a Von Neumann model to a neural model. NPUs achieve an average 2.3× speedup and 3.0× energy savings for general-purpose approximate programs. This new class of accelerators shows that significant performance and efficiency gains are possible when the abstraction of full accuracy is relaxed in general-purpose computing. [ABSTRACT FROM PUBLISHER]
Copyright of IEEE Micro is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 87999807
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: This work proposes an approximate algorithmic transformation and a new class of accelerators, called neural processing units (NPUs). NPUs leverage the approximate algorithmic transformation that converts regions of code from a Von Neumann model to a neural model. NPUs achieve an average 2.3× speedup and 3.0× energy savings for general-purpose approximate programs. This new class of accelerators shows that significant performance and efficiency gains are possible when the abstraction of full accuracy is relaxed in general-purpose computing. [ABSTRACT FROM PUBLISHER]
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  Data: <i>Copyright of IEEE Micro is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/MM.2013.28
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        Text: English
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      – SubjectFull: Central processing units
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      – SubjectFull: Approximation theory
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      – TitleFull: Neural Acceleration for General-Purpose Approximate Programs.
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              Text: May2013
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