Supervised Machine Learning for Training a Neural Network as 5:2 Compressor

Abstract

Machine Learning has achieved substantial development in numerous applications like image processing, pattern recognition, approximate computing etc. This paper interlinks supervised machine learning algorithm and VLSI architectures to train a neural network as exact and approximate 5:2 compressors. Probabilistic pruning type of approximation technique has been employed on the exact 5:2 compressor. This approximation technique on compressors reduces the power consumption with variation in the outputs without affecting the error limit. The simulation of 5:2 compressors and training of neural network using machine learning algorithm has been done using Spectre simulator of Cadence Design Systems at 45nm CMOS technology node and Keras library with TensorFlow background respectively.