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An inexact 6-transistor full adder (FA) cell is presented. It is low-power and gives full-swing outputs because of the applied gate diffusion input (GDI) along with the dynamic threshold (DT) techniques. The FA which has three errors is used in a 10-transistor inexact 4:2 compressor based on the stacking circuit idea. The implementation by a 16-nm carbon nanotube field-effect transistor (CNTFET) technology shows a small area of 0.027 and $0.045~\mu \text{m}^{\mathrm{ 2}}$ for the FA and compressor, respectively. A new approximate sum of absolute differences (SADs) block is presented by the FA and compressor. The average power, power-delay-product (PDP), and peak signal-to-noise ratio (PSNR) of the SAD are 6.80 mW, 33.86 nJ, and 42.98 dB, respectively, which confirm its efficiency for bioimages difference detection. [ABSTRACT FROM AUTHOR]

• Using the carbon nanotube field-effect transistors technology to design approximate arithmetic circuits. • The gate diffusion input and dynamic threshold techniques are integrated to attain efficient circuits. • The presented approximate full adders show reliable performance during image processing. • The approximate full adders applied in bioimage processing. • Fat and water can be detected in bioimages by the presented approximate cells. Two new approximate full adders (FAs) are proposed by the multiplexers (MUXs) and OR gates with the gate diffusion input (GDI) technique. The cells are named GDI-based MUX approximate FAs (GMAFAs), GMAFA1, and GMAFA2. The threshold voltage drop of the GDI-based circuits is solved by carbon nanotube field-effect transistors (CNTFETs) technology and the dynamic threshold (DT) technique. The FAs have accurate C out , approximate Sum, and a low number of transistors. They are low-power, high-speed, and energy-efficient for ripple-carry adders (RCAs). The GMAFA1 and GMAFA2 have a total area of 0.068 µm2 and 0.119 µm2, respectively, which demonstrate 27.8% and 54.55% improvement in power-delay-area-product (PDAP) in comparison with the best references. The results of error distance (ED), normalized mean error distance (NMED), power signal-to-noise ratio (PSNR), and energy-saving confirm the accuracy of the presented cells for image processing applications. [Display omitted] [ABSTRACT FROM AUTHOR]

Three new approximate subtractors are presented based on the gate diffusion input (GDI) technique which benefits from the dynamic threshold (DT) technique using carbon nanotube field-effect transistors (CNTFETs) technology. Proposed-1, Proposed-2, and Proposed-3 subtractors have 10, 8, and 6 transistors with a total area of 0.289 μm2, 0.123 μm2, and 0.065 μm2, respectively. In Proposed-1 and Proposed-2, only XOR and F1 gates are used, which respectively have 2 and 4 errors. To solve the non-full-swing defects of these two circuits, only three F2-GDI gates are used in the Proposed-3, which has 4 errors. The Proposed-1 gives 0.25 as error distance (ED) while two other circuits with a lower area have an ED of 0.5. The Proposed-3 has a simple, low-power, and ground-free structure. The subtractors are implemented in an 8 to 4 non-restoring divider in four different configurations including vertical, horizontal, square, and triangular. The drivers based on Proposed-2 and Proposed-3 are the best circuits with average power-delay-product (PDP) savings of 68.16% and 57.43%, respectively. Finally, the dividers are used in the change detection of medical images, where, the proposed circuits show better circuitry performance and image quality production. [ABSTRACT FROM AUTHOR]