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 …

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 …

Binary neural networks (BNNs) have 1-bit weights and activations. Such networks are well
suited for FPGAs, as their dominant computations are bitwise arithmetic and the memory …

Abstract Optimization of Binarized Neural Networks (BNNs) currently relies on real-valued
latent weights to accumulate small update steps. In this paper, we argue that these latent …

Abstract Optimization of Top-1 ImageNet promotes enormous networks that may be
impractical in inference settings. Binary neural networks (BNNs) have the potential to …