Deep learning (DL) has recently changed the development of intelligent systems and is
widely adopted in many real-life applications. Despite their various benefits and potentials …

This paper presents an extensive literature review on Binary Neural Network (BNN). BNN
utilizes binary weights and activation function parameters to substitute the full-precision …

This chapter provides approaches to the problem of quantizing the numerical values in deep
Neural Network computations, covering the advantages/disadvantages of current methods …

The binary neural network, largely saving the storage and computation, serves as a
promising technique for deploying deep models on resource-limited devices. However, the …

In this paper, we propose several ideas for enhancing a binary network to close its accuracy
gap from real-valued networks without incurring any additional computational cost. We first …

Weight and activation binarization is an effective approach to deep neural network
compression and can accelerate the inference by leveraging bitwise operations. Although …

This paper shows how to train binary networks to within a few percent points ($\sim 3-5\% $)
of the full precision counterpart. We first show how to build a strong baseline, which already …

Abstract The best performing Binary Neural Networks (BNNs) are usually attained using
Adam optimization and its multi-step training variants. However, to the best of our …

Binary neural networks (BNNs) have received increasing attention due to their superior
reductions of computation and memory. Most existing works focus on either lessening the …

In this paper, we propose a channel-wise interaction based binary convolutional neural
network learning method (CI-BCNN) for efficient inference. Conventional methods apply …