The Hidden Cost of Functional Approximation Against Careful Data Sizing – A Case Study

Abstract : Many applications are error-resilient, allowing for the introduction of approximations in the calculations, as long as a certain accuracy target is met. Traditionally, fixed-point arithmetic is used to relax accuracy, by optimizing the bit-width. This arithmetic leads to important benefits in terms of delay, power and area. Lately, several hardware approximate operators were invented, seeking the same performance benefits. However, a fair comparison between the usage of this new class of operators and classical fixed-point arithmetic with careful truncation or rounding, has never been performed. In this paper, we first compare approximate and fixed-point arithmetic operators in terms of power, area and delay, as well as in terms of induced error, using many state-of-the-art metrics and by emphasizing the issue of data sizing. To perform this analysis, we developed a design exploration framework, APXPERF, which guarantees that all operators are compared using the same operating conditions. Moreover, operators are compared in several classical real-life applications leveraging relevant metrics. In this paper, we show that considering a large set of parameters, existing approximate adders and multipliers tend to be dominated by truncated or rounded fixed-point ones. For a given accuracy level and when considering the whole computation data-path, fixed-point operators are several orders of magnitude more accurate while spending less energy to execute the application. A conclusion of this study is that the entropy of careful sizing is always lower than approximate operators, since it require significantly less bits to be processed in the data-path and stored. Approximated data therefore always contain on average a greater amount of costly erroneous, useless information.
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Contributor : Olivier Sentieys <>
Submitted on : Wednesday, December 28, 2016 - 4:03:31 PM
Last modification on : Friday, September 13, 2019 - 9:49:43 AM
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  • HAL Id : hal-01423147, version 1


Benjamin Barrois, Olivier Sentieys, Daniel Ménard. The Hidden Cost of Functional Approximation Against Careful Data Sizing – A Case Study. Design, Automation & Test in Europe Conference & Exhibition (DATE 2017), 2017, Lausanne, France. ⟨hal-01423147⟩



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