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- W3048414096 endingPage "2135" @default.
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- W3048414096 abstract "Approximate computing has emerged as a new paradigm for high-performance and energy-efficient design of circuits and systems. For the many approximate arithmetic circuits proposed, it has become critical to understand a design or approximation technique for a specific application to improve performance and energy efficiency with a minimal loss in accuracy. This article aims to provide a comprehensive survey and a comparative evaluation of recently developed approximate arithmetic circuits under different design constraints. Specifically, approximate adders, multipliers, and dividers are synthesized and characterized under optimizations for performance and area. The error and circuit characteristics are then generalized for different classes of designs. The applications of these circuits in image processing and deep neural networks indicate that the circuits with lower error rates or error biases perform better in simple computations, such as the sum of products, whereas more complex accumulative computations that involve multiple matrix multiplications and convolutions are vulnerable to single-sided errors that lead to a large error bias in the computed result. Such complex computations are more sensitive to errors in addition than those in multiplication, so a larger approximation can be tolerated in multipliers than in adders. The use of approximate arithmetic circuits can improve the quality of image processing and deep learning in addition to the benefits in performance and power consumption for these applications." @default.
- W3048414096 created "2020-08-18" @default.
- W3048414096 creator A5005550142 @default.
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- W3048414096 creator A5042730393 @default.
- W3048414096 creator A5053681670 @default.
- W3048414096 creator A5064687523 @default.
- W3048414096 date "2020-12-01" @default.
- W3048414096 modified "2023-10-06" @default.
- W3048414096 title "Approximate Arithmetic Circuits: A Survey, Characterization, and Recent Applications" @default.
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