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Approximation on Data Flow Graph Execution for Energy Efficiency

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Approximate Computing

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Abstract

Data flow graph (DFG) is a popular model for software and its execution. It consists of a list of arithmetic operations without conditionals and their dependencies. Completion time and energy consumption are two main objectives for DFG optimization. In this chapter, we discuss approximation methods at different levels of DFG that can reduce energy consumption with a guaranteed quantity of results. First, we consider a probabilistic design framework that approximates the application by intentionally terminating certain DFG executions before reaching the deadline. Second, we demonstrate a real-time estimation-and-recomputing approach that executes the non-critical parts of the DFG with approximation. Finally, we use the floating-point logarithmic operation as an example to show how to optimize data bit width based on the DFG model.

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Authors and Affiliations

  1. University of Maryland, College Park, MD, USA

    Qian Xu & Gang Qu

  2. Morgan State University, Baltimore, MD, USA

    Md Tanvir Arafin

Authors
  1. Qian Xu
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  2. Md Tanvir Arafin
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  3. Gang Qu
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Corresponding author

Correspondence to Gang Qu .

Editor information

Editors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Weiqiang Liu

  2. Northeastern University, Boston, MA, USA

    Fabrizio Lombardi

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Xu, Q., Arafin, M.T., Qu, G. (2022). Approximation on Data Flow Graph Execution for Energy Efficiency. In: Liu, W., Lombardi, F. (eds) Approximate Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-98347-5_9

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  • DOI: https://doi.org/10.1007/978-3-030-98347-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98346-8

  • Online ISBN: 978-3-030-98347-5

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