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

Neural network binarization accelerates deep models by quantizing their weights and
activations into 1-bit. However, there is still a huge performance gap between Binary Neural …

Relying on the premise that the performance of a binary neural network can be largely
restored with eliminated quantization error between full-precision weight vectors and their …

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

Abstract We introduce Larq Compute Engine (LCE), a state-of-the-art Binarized Neural
Network (BNN) inference engine, and use this framework to investigate several important …

Convolutional neural networks are able to learn realistic image priors from numerous
training samples in low-level image generation and restoration. We show that, for high-level …

Multi-task learning for dense prediction has emerged as a pivotal area in computer vision
enabling simultaneous processing of diverse yet interrelated pixel-wise prediction tasks …

The ever-growing computational demands of increasingly complex machine learning
models frequently necessitate the use of powerful cloud-based infrastructure for their …