PresB-Net: parametric binarized neural network with learnable activations and shuffled grouped convolution

In this study, we present a novel performance-enhancing binarized neural network model called PresB-Net: Parametric Binarized Neural Network. A binarized neural network (BNN) model can achieve fast output computation with low hardware costs by using binarized weights and features. However, performance degradation is the most critical problem in BNN models. Our PresB-Net combines several state-of-the-art BNN structures including the learnable activation with additional trainable parameters and shuffled grouped convolution. Notably, we propose a new normalization approach, which reduces the imbalance between the shuffled groups occurring in shuffled grouped convolutions. Besides, the proposed normalization approach helps gradient convergence so that the unstableness of the learning can be amortized when applying the learnable activation. Our novelBNNmodel enhances the classification performance compared with other existing BNN models. Notably, the proposed PresB-Net-18 achieves 73.84% Top-1 inference accuracy for the CIFAR-100 dataset, outperforming other existing counterparts.

Automated summary

PresB-Net is a novel performance-enhancing binarized neural network model that combines several state-of-the-art structures and introduces a new normalization approach. Achieving 73.84% Top-1 inference accuracy on the CIFAR-100 dataset, PresB-Net-18 outperforms other existing counterparts.