The ever-increasing demand for higher computing performance while conserving energy resources has been a constant challenge for emerging applications. Approximate computing (AC) has emerged as a promising approach to address this challenge. AC has sought to replace complex, traditional, power-hungry data processing blocks with simpler, low-gate counts. These approximations have introduced inaccuracies into processed data, but, in return, they have offered substantial reductions in power consumption and chip area. This paradigm shift has had the potential to create more energy-efficient systems that are tailored to current and future application trends [1,2].The input data has inherently contained noise in many real-world scenarios, such as image and video processing, multimedia systems, and data processing for recognition and clustering. The methods engaged in these data processing tasks have often been probabilistic or statistical. Given the probabilistic