Methods to evaluate accuracy-energy trade-off in operator-level approximate computing

Abstract : The physical limits being reached in silicon-based computing, new ways have to be found to overcome the predicted end of Moore's law. Many applications can tolerate approximations in their computations at several levels without degrading the quality of their output, or degrading it in an acceptable way. This thesis focuses on approximate arithmetic architectures to seize this opportunity. Firstly, a critical study of state-of-the-art approximate adders and multipliers is presented. Then, a model for fixed-point error propagation leveraging power spectral density is proposed, followed by a model for bitwise-error rate propagation of approximate operators. Approximate operators are then used for the reproduction of voltage over-scaling effects in exact arithmetic operators. Leveraging our open-source framework ApxPerf and its synthesizable template-based C++ libraries apx_fixed for approximate operators, and ct_float for low-power floating-point arithmetic, two consecutive studies are proposed leveraging complex signal processing applications. Firstly, approximate operators are compared to fixed-point arithmetic, and the superiority of fixed-point is highlighted. Secondly, fixed-point is compared to small-width floating-point in equivalent conditions. Depending on the applicative conditions, floating-point shows an unexpected competitiveness compared to fixed-point. The results and discussions of this thesis give a fresh look on approximate arithmetic and suggest new directions for the future of energy-efficient architectures.
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Submitted on : Thursday, March 29, 2018 - 4:06:09 PM
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Benjamin Barrois. Methods to evaluate accuracy-energy trade-off in operator-level approximate computing. Computer Arithmetic. Université Rennes 1, 2017. English. ⟨NNT : 2017REN1S097⟩. ⟨tel-01665015v2⟩

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