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 …

Deep learning systems have been successfully applied to Euclidean data such as images,
video, and audio. In many applications, however, information and their relationships are …

Over the years, accelerating neural networks with quantization has been widely studied.
Unfortunately, prior efforts with diverse precisions (eg, 1-bit weights and 2-bit activations) are …

Neural network models, celebrated for their outstanding scalability and computational
capabilities, have demonstrated remarkable performance across various fields such as …

Many convolutional neural network (CNN) accelerators are proposed to exploit the sparsity
of the networks recently to enjoy the benefits of both computation and memory reduction …

In general, deep neural networks (DNNs) are seriously overparameterized with enormous
hardware resources demanded, which creates a heavy burden for inference applications …