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 …

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 chapter provides approaches to the problem of quantizing the numerical values in deep
Neural Network computations, covering the advantages/disadvantages of current methods …

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 …

In this paper, we propose several ideas for enhancing a binary network to close its accuracy
gap from real-valued networks without incurring any additional computational cost. We first …

Weight and activation binarization is an effective approach to deep neural network
compression and can accelerate the inference by leveraging bitwise operations. Although …

This paper shows how to train binary networks to within a few percent points ($\sim 3-5\% $)
of the full precision counterpart. We first show how to build a strong baseline, which already …

Abstract Binary Neural Network (BNN) shows its predominance in reducing the complexity of
deep neural networks. However, it suffers severe performance degradation. One of 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 …

Abstract Recently low-bit (eg, 8-bit) network quantization has been extensively studied to
accelerate the inference. Besides inference, low-bit training with quantized gradients can …