Abstract
This paper proposes a series of approximate square root circuit designs with high accuracy, low latency, low area, and low power dissipation requirements. The proposed designs are constructed using an array of controlled add–subtract cell elements with both exact and approximate versions. The utility of the proposed designs are evaluated by utilizing them in an example image contrast enhancement application with demonstrably satisfactory results and large peak signal-to-noise ratios and structural similarity values. The accuracy and hardware characteristics of the proposed square root designs are also analyzed and compared with previously proposed state-of-the-art approximate square root designs. When applied to a 16-bit radicand (the number under the square root symbol), the proposed designs have the lowest error rates, normalized mean error distances, and mean relative error distances by at least 1.8x when compared to all previous methods using the same number of approximate cells. When the designs were synthesized using Synopsys Design Compiler with a 28 nm bulk CMOS process, the delay, area, power, and power-delay-product characteristics outperform all previous designs in all but a few cases. These results demonstrate that the proposed designs permit the use of a flexible range of approximate designs with varying accuracy and hardware overhead characteristics, and a suitable design can be selected based on the user design requirements.
Highlights
Academic Editors: Leonardo Pantoli, Artificial intelligence applications using the latest deep neural network designs typically involve massive amounts of time critical computations
Since this paper primarily targets arithmetic circuits for error-resilient applications that work with fixed point numbers such as image pixels, it is assumed that only nonnegative numbers are used for the radicand and square root
The variable p is used to denote the number of columns that are replaced with approximate Controlled Add–Subtract (CAS) cells
Summary
Academic Editors: Leonardo Pantoli, Artificial intelligence applications using the latest deep neural network designs typically involve massive amounts of time critical computations. Such applications are error resilient [1], which means that they can tolerate errors in computations without significant overall accuracy loss. The unique nonnegative square root (either 0 or a positive number) of a nonnegative radicand is referred to as the principle square root. Since this paper primarily targets arithmetic circuits for error-resilient applications that work with fixed point numbers such as image pixels, it is assumed that only nonnegative numbers are used for the radicand and square root. Since only nonnegative integers are used, the square root of a √
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