Abstract: This disclosure relates generally to neural network inferencing, and more particularly, to a method and system for neural network inferencing in logarithmic domain. The conventional techniques include training a neural network in logarithmic domain and performing inferencing. This leads to less accuracy, challenge in converting large models and unable to perform optimization. The present disclosure converts a pre-trained neural network into logarithmic domain using a bit manipulation based logarithm number system technique wherein the neural network is pre-trained in real time or in logarithmic domain. The method converts the weights, neural network layers and activation function into logarithmic domain. The method uses a 32-bit integer variable to store a logarithm number which leads to memory efficiency. The disclosed method is used for inferencing of convolutional neural network for natural language processing, image recognition and so on.