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Exploiting approximate computing for low-cost fault tolerant architectures

Published: 26 August 2019 Publication History

Abstract

This work investigates how the approximate computing paradigm can be exploited to provide low-cost fault tolerant architectures. In particular, we focus on the implementation of Approximate Triple Modular Redundancy (ATMR) designs using the precision reduction technique. The proposed method is applied to two benchmarks and a multitude of ATMR designs with different degrees of approximation. The benchmarks are implemented on a Xilinx Zynq-7000 APSoC FPGA through high-level synthesis and evaluated concerning area usage and the inaccuracy caused by approximation. Fault injection experiments are performed by flipping bits of the FPGA configuration bitstream. Results show that the proposed approximation method can decrease the DSP usage of the hardware implementation up to 80% and the number of sensitive configuration bits up to 75% while maintaining an accuracy of more than 99.96%.

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cover image ACM Conferences
SBCCI '19: Proceedings of the 32nd Symposium on Integrated Circuits and Systems Design
August 2019
204 pages
ISBN:9781450368445
DOI:10.1145/3338852
© 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 26 August 2019

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Author Tags

  1. approximate computing
  2. fault tolerance
  3. high-level synthesis
  4. performance
  5. reconfigurable systems

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Cited By

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  • (2023)Approximation Opportunities in Edge Computing Hardware: A Systematic Literature ReviewACM Computing Surveys10.1145/357277255:12(1-49)Online publication date: 3-Mar-2023
  • (2023)A green isolated word sign language recognition system based on geodesic metric space2023 International Conference on Cyberworlds (CW)10.1109/CW58918.2023.00049(274-281)Online publication date: 3-Oct-2023
  • (2022)Towards Approximate Computing for Achieving Energy vs. Accuracy Trade-offs2022 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE54114.2022.9774538(632-635)Online publication date: 14-Mar-2022
  • (2022)Analyzing Reduced Precision Triple Modular Redundancy Under Proton IrradiationIEEE Transactions on Nuclear Science10.1109/TNS.2022.315208869:3(470-477)Online publication date: Mar-2022
  • (2022)Approximate Computing for Fault Tolerance Mechanisms for Safety-Critical ApplicationsApproximate Computing Techniques10.1007/978-3-030-94705-7_13(387-414)Online publication date: 3-Jan-2022
  • (2021)Approximation-based Fault Tolerance in Image Processing ApplicationsIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2021.3100623(1-1)Online publication date: 2021
  • (2021)Lightweight Dual Modular Redundancy through Approximate Computing2021 XI Brazilian Symposium on Computing Systems Engineering (SBESC)10.1109/SBESC53686.2021.9628356(1-8)Online publication date: 22-Nov-2021
  • (2021)SAM: A Segmentation based Approximate Multiplier for Error Tolerant Applications2021 IEEE International Symposium on Circuits and Systems (ISCAS)10.1109/ISCAS51556.2021.9401266(1-5)Online publication date: May-2021
  • (2021)Physical unclonable function based proof-carrying approximate circuitsMaterials Today: Proceedings10.1016/j.matpr.2021.01.608Online publication date: Mar-2021
  • (2020)Highly-Reliable Approximate Quadruple Modular Redundancy with Approximation-Aware Voting2020 32nd International Conference on Microelectronics (ICM)10.1109/ICM50269.2020.9331771(1-4)Online publication date: 14-Dec-2020
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