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Circuit-Level Techniques for Logic and Memory Blocks in Approximate Computing Systemsx.

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Title: Circuit-Level Techniques for Logic and Memory Blocks in Approximate Computing Systemsx.
Authors: Amanollahi, Saba1 s.amanollahi@ut.ac.ir, Kamal, Mehdi1 mehdikamal@ut.ac.ir, Afzali-Kusha, Ali1 afzali@ut.ac.ir, Pedram, Massoud2 pedram@usc.edu
Source: Proceedings of the IEEE. Dec2020, Vol. 108 Issue 12, p2150-2177. 28p.
Subjects: Nonvolatile memory, Data warehousing, Memory, Random access memory, Cloud storage
Abstract: This article presents an overview of circuit-level techniques used for approximate computing (AC), including both computation and data storage units. After providing some background concept and methodology review, this article proceeds to provide a detailed review of prior art in circuit-level approximation techniques for data path and memory. The focus is on identifying key circuit-level approximation techniques that are applicable to the computational blocks in general and for both volatile and nonvolatile memory circuit technologies. Emphasis is also placed on the error metrics used to assess the output quality of approximate compute and memory units and whether the accuracy setting is dynamically reconfigurable. This article is concluded with a summary of the key distinguishing features of the reviewed prior art. [ABSTRACT FROM AUTHOR]
Copyright of Proceedings of the IEEE 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.)
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  Data: Circuit-Level Techniques for Logic and Memory Blocks in Approximate Computing Systemsx.
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  Data: <searchLink fieldCode="AR" term="%22Amanollahi%2C+Saba%22">Amanollahi, Saba</searchLink><relatesTo>1</relatesTo><i> s.amanollahi@ut.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Kamal%2C+Mehdi%22">Kamal, Mehdi</searchLink><relatesTo>1</relatesTo><i> mehdikamal@ut.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Afzali-Kusha%2C+Ali%22">Afzali-Kusha, Ali</searchLink><relatesTo>1</relatesTo><i> afzali@ut.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Pedram%2C+Massoud%22">Pedram, Massoud</searchLink><relatesTo>2</relatesTo><i> pedram@usc.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Proceedings+of+the+IEEE%22">Proceedings of the IEEE</searchLink>. Dec2020, Vol. 108 Issue 12, p2150-2177. 28p.
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  Data: <searchLink fieldCode="DE" term="%22Nonvolatile+memory%22">Nonvolatile memory</searchLink><br /><searchLink fieldCode="DE" term="%22Data+warehousing%22">Data warehousing</searchLink><br /><searchLink fieldCode="DE" term="%22Memory%22">Memory</searchLink><br /><searchLink fieldCode="DE" term="%22Random+access+memory%22">Random access memory</searchLink><br /><searchLink fieldCode="DE" term="%22Cloud+storage%22">Cloud storage</searchLink>
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  Label: Abstract
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  Data: This article presents an overview of circuit-level techniques used for approximate computing (AC), including both computation and data storage units. After providing some background concept and methodology review, this article proceeds to provide a detailed review of prior art in circuit-level approximation techniques for data path and memory. The focus is on identifying key circuit-level approximation techniques that are applicable to the computational blocks in general and for both volatile and nonvolatile memory circuit technologies. Emphasis is also placed on the error metrics used to assess the output quality of approximate compute and memory units and whether the accuracy setting is dynamically reconfigurable. This article is concluded with a summary of the key distinguishing features of the reviewed prior art. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Proceedings of the IEEE 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/JPROC.2020.3020792
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        Text: English
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        Type: general
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      – SubjectFull: Memory
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              Text: Dec2020
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