A Neural Network trained to perform as an Imprecise 4:2 Compressor using Machine Learning Algorithm

Abstract

In many fields, such as speech recognition, image processing, Internet of Things (IoT) systems, arithmetic circuits, etc., artificial neural networks have become a popular solution for a wide range of problems. In this research work, a 4:2 compressor-based arithmetic circuit is considered and an approximation technique called probabilistic pruning is applied to it. This proposed compressor is compared with various existing approximate 4:2 compressors. Training of a neural network as proposed inexact 4:2 compressor is performed using a supervised machine learning algorithm. The accuracy values of the proposed compressor are acceptable at architectural and neural network levels. The absolute difference between the train set and the predicted test set accuracy values obtained is very less (1.73%) when compared with the other (inexact 4:2 compressors) trained deep neural network models.