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X-CGRA: An Energy-Efficient Approximate Coarse-Grained Reconfigurable Architecture.

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Title: X-CGRA: An Energy-Efficient Approximate Coarse-Grained Reconfigurable Architecture.
Authors: Akbari, Omid1 o.akbari@modares.ac.ir, Kamal, Mehdi1 mehdikamal@ut.ac.ir, Afzali-Kusha, Ali1 afzali@ut.ac.ir, Pedram, Massoud2 pedram@usc.edu, Shafique, Muhammad3 muhammad.shafique@tuwien.ac.at
Source: IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems. Sep2020, Vol. 39 Issue 10, p2558-2571. 14p.
Subjects: Video processing, Energy consumption, Signal processing, Quality of service
Abstract: In this article, we present an energy-efficient approximate CGRA (X-CGRA). Instead of conventional exact arithmetic units, it employs configurable approximate adders and multipliers in the so-called quality-scalable processing elements (QSPEs). Furthermore, the structure and functionality of the other architectural components, like context memory, are modified based on the quality-scalable operating modes of the QSPEs. The quality reconfigurability of the X-CGRA makes it amenable for both error-resilient and nonresilient applications. To map the applications on the X-CGRA, a mapping technique is proposed that efficiently utilizes the QSPEs and selects appropriate approximation modes in order to lower the energy consumption while satisfying a user-defined quality constraint. We evaluate the efficacy of our X-CGRA for several benchmark applications from different domains, including image/video processing, signal processing, and scientific computations. Different sizes of X-CGRA are synthesized using a 15-nm FinFET technology. For these benchmarks, the results indicate energy consumption reduction of up to 3.21 × compared to those of a typical exact CGRA, at the cost of 4% quality loss. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems 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: X-CGRA: An Energy-Efficient Approximate Coarse-Grained Reconfigurable Architecture.
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  Data: <searchLink fieldCode="AR" term="%22Akbari%2C+Omid%22">Akbari, Omid</searchLink><relatesTo>1</relatesTo><i> o.akbari@modares.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><br /><searchLink fieldCode="AR" term="%22Shafique%2C+Muhammad%22">Shafique, Muhammad</searchLink><relatesTo>3</relatesTo><i> muhammad.shafique@tuwien.ac.at</i>
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  Data: <searchLink fieldCode="DE" term="%22Video+processing%22">Video processing</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+of+service%22">Quality of service</searchLink>
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  Data: In this article, we present an energy-efficient approximate CGRA (X-CGRA). Instead of conventional exact arithmetic units, it employs configurable approximate adders and multipliers in the so-called quality-scalable processing elements (QSPEs). Furthermore, the structure and functionality of the other architectural components, like context memory, are modified based on the quality-scalable operating modes of the QSPEs. The quality reconfigurability of the X-CGRA makes it amenable for both error-resilient and nonresilient applications. To map the applications on the X-CGRA, a mapping technique is proposed that efficiently utilizes the QSPEs and selects appropriate approximation modes in order to lower the energy consumption while satisfying a user-defined quality constraint. We evaluate the efficacy of our X-CGRA for several benchmark applications from different domains, including image/video processing, signal processing, and scientific computations. Different sizes of X-CGRA are synthesized using a 15-nm FinFET technology. For these benchmarks, the results indicate energy consumption reduction of up to 3.21 × compared to those of a typical exact CGRA, at the cost of 4% quality loss. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IEEE Transactions on Computer-Aided Design of Integrated Circuits & Systems 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/TCAD.2019.2937738
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      – SubjectFull: Signal processing
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              Text: Sep2020
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