The increasing complexity of of CNN for achieving higher accuracy, require high computation power for training the and testing the model. Therefore, optimized CNN architectures are required for low power applications. Since most of computation in a conventional convolutional neural network is floating point multiply-and-accumulate (MAC) operation, several researchers have proposed some precision-reduced approach to simplify the computations of CNN.