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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]
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Database: Engineering Source
Description
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]
ISSN:02780070
DOI:10.1109/TCAD.2019.2937738