The research advances concerning the typical architectures of convolutional neural
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …

Implementations of artificial neural networks that borrow analogue techniques could
potentially offer low-power alternatives to fully digital approaches,–. One notable example is …

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 …

Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

When quantizing neural networks, assigning each floating-point weight to its nearest fixed-
point value is the predominant approach. We find that, perhaps surprisingly, this is not the …

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 …

Hardware-friendly network quantization (eg, binary/uniform quantization) can efficiently
accelerate the inference and meanwhile reduce memory consumption of the deep neural …

Reducing bit-widths of activations and weights of deep networks makes it efficient to
compute and store them in memory, which is crucial in their deployments to resource-limited …

Network quantization aims at reducing bit-widths of weights and/or activations, particularly
important for implementing deep neural networks with limited hardware resources. Most …