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 …

The booming development of deep learning applications and services heavily relies on
large deep learning models and massive data in the cloud. However, cloud-based deep …

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 rapid development of large pre-trained language models has greatly increased the
demand for model compression techniques, among which quantization is a popular solution …

Neural network quantization enables the deployment of large models on resource-
constrained devices. Current post-training quantization methods fall short in terms of …

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 …

Spiking neural networks (SNNs) with leaky integrate and fire (LIF) neurons, can be operated
in an event-driven manner and have internal states to retain information over time, providing …

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 …

Noisy intermediate-scale quantum (NISQ) devices are restricted by their limited number of
qubits and their short decoherence times. An approach addressing these problems is …

To bridge the ever-increasing gap between deep neural networks' complexity and hardware
capability, network quantization has attracted more and more research attention. The latest …