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Fine-Grain Back Biasing for the Design of Energy-Quality Scalable Operators

Abstract : Energy-quality scalable systems are a promising solution to cope with the small energy budgets and high processing demands of mobile and Internet of Things applications. These systems leverage the error resilience of applications to obtain high energy efficiency, at the expense of tolerable reductions in the output quality. Hardware datapath operators able to reconfigure their precision and power consumption at runtime are key components of such systems. However, most implementations of these operators require manual, architecture-specific modifications and tend to have large power overheads compared to standard designs, when working at maximum precision. One promising design-independent alternative is dynamic voltage and accuracy scaling, whose adoption, however, is hindered by incompatibilities with standard design flows. In this paper, we propose a new methodology for the design of energy-quality scalable operators; our solution leverages runtime tuning of transistors threshold voltages to obtain a fine-grain control of the speed and power consumption of standard-cells within an operator. Thanks to the additional flexibility provided by this fine-grain knob, our method overcomes the main limitations of previous solutions, at the cost of a small area overhead. We demonstrate our approach on a 28 nm FDSOI technology; by exploiting the strong effect of back-gate biasing on threshold voltage, we achieve a power consumption reduction of more than 40% compared to the state-of-the-art, for the same precision.
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https://hal-cea.archives-ouvertes.fr/cea-02186476
Contributor : Marianne Leriche <>
Submitted on : Wednesday, July 17, 2019 - 12:22:28 PM
Last modification on : Thursday, June 11, 2020 - 5:04:09 PM

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Daniele Jahier Pagliari, Yves Durand, David Coriat, Edith Beigné, Enrico Macii, et al.. Fine-Grain Back Biasing for the Design of Energy-Quality Scalable Operators. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, IEEE, 2019, 38 (6), pp.1042-1055. ⟨10.1109/TCAD.2018.2834400⟩. ⟨cea-02186476⟩

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