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 …

Network quantization is essential for deploying deep models to IoT devices due to its high
efficiency. Most existing quantization approaches rely on the full training datasets and the …

This paper studies the Binary Neural Networks (BNNs) in which weights and activations are
both binarized into 1-bit values, thus greatly reducing the memory usage and computational …

Network binarization emerges as one of the most promising compression approaches
offering extraordinary computation and memory savings by minimizing the bit-width …

Abstract Spiking Neural Networks (SNNs) can be energy efficient alternatives to commonly
used deep neural networks (DNNs). However, computation over multiple timesteps …

Abstract Model binarization is an effective method of compressing neural networks and
accelerating their inference process, which enables state-of-the-art models to run on …

Recently, there have been tremendous efforts in develo** lightweight Deep Neural
Networks (DNNs) with satisfactory accuracy, which can enable the ubiquitous deployment of …

Binarization of neural network models is considered as one of the promising methods to
deploy deep neural network models on resource-constrained environments such as mobile …

Deep neural networks executing with low precision at inference time can gain acceleration
and compression advantages over their high-precision counterparts, but need to overcome …