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Binarized Neural Networks are paving a way towards the deployment of deep neural networks with less memory and computation. In this report, we present a detailed study on the paper titled "Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization" which proposes a new optimization method for training BNN called BOP. We first investigate the effect of using latent weights in BNN for analyzing prediction performance in terms of accuracy. Next, a comprehensive ablation study on hyperparameters is provided. Finally, we explore the usability of BNN in denoising autoencoders. Code for all our experiments are available at https://github.com/nancy-nayak/rethinking-bnn/