Automation techniques and machine learning algorithms are playing a crucial role in almost all fields in recent times. In this research, a 4:2 compressor circuit is approximated using the probabilistic pruning technique. An artificial neural network is designed for the proposed 4:2 compressor and is trained to obtain the train and test accuracies. The neural network with equal train and test accuracies has been considered as the best approximate circuit. The training of the neural network has been performed using a supervised machine learning algorithm by applying truth table of the proposed approximate 4:2 compressor as the dataset. The proposed compressor has only 19 transistors and consumes less energy i.e.,0.2015 nJ with less silicon area of 14.36 um². The performance of the Dadda multiplier is improved by replacing the proposed approximate 4:2 compressor into its partial product reduction stage.

近年来，自动化技术和机器学习算法在几乎所有领域中都发挥着至关重要的作用。在这项研究中，使用概率修剪技术来近似4：2压缩机回路。为拟议的4：2压缩机设计了一个人工神经网络，并对其进行了训练以获得训练和测试精度。具有相同训练和测试精度的神经网络被认为是最佳的近似电路。通过使用拟议的近似4：2压缩器的真值表作为数据集，使用监督机器学习算法对神经网络进行了训练。拟议的压缩机只有19个晶体管，消耗的能量更少，即0.2015 nJ，硅面积减少了14.36 um ²。通过将建议的近似4：2压缩机替换为其部分产品缩减阶段，可以提高Dadda乘法器的性能。