In applications such as image processing and machine learning, imprecision can be tolerated because of the nature of the application itself or the limitation of human senses. By using the approximate computation in parts of imprecision-tolerant applications, where the output quality can be slightly degraded, significant power, delay, or area reductions can be achieved. In this paper, three approximate full adders with reasonable accuracy, low power, and low delay are proposed. The effects of die-to-die (D2D) process variation on the threshold voltage of approximate full adders have been evaluated, and a method has been proposed to reduce the effects of variability. For evaluating the accuracy and the variability, these approximate full adders have been used and analyzed in the ripple carry adder structure and image Sharpening algorithm. In terms of power-delay-product (PDP), accuracy, and area for uniformly distributed inputs, one of the presented approximate full adders exhibits the best performance, and another one shows the best peak-signal-to-noise ratio (PSNR) for real images.

在图像处理和机器学习等应用中，由于应用本身的性质或人类感官的限制，不精确是可以容忍的。通过在部分不精确容忍应用中使用近似计算，其中输出质量可能会略微降低，可以实现显着的功率、延迟或面积减少。在本文中，提出了三种具有合理精度、低功耗和低延迟的近似全加器。已经评估了裸片到裸片 (D2D) 工艺变化对近似全加器阈值电压的影响，并提出了一种减少可变性影响的方法。为了评估精度和可变性，这些近似全加器已在纹波进位加法器结构和图像锐化算法中使用和分析。