The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

In this community review report, we discuss applications and techniques for fast machine
learning (ML) in science—the concept of integrating powerful ML methods into the real-time …

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

Neural network pruning offers a promising prospect to facilitate deploying deep neural
networks on resource-limited devices. However, existing methods are still challenged by the …

In this paper, we analyze two popular network compression techniques, ie filter pruning and
low-rank decomposition, in a unified sense. By simply changing the way the sparsity …

We propose a simple but effective data-driven channel pruning algorithm, which
compresses deep neural networks in a differentiable way by exploiting the characteristics of …

Abstract Knowledge distillation conditioned on intermediate feature representations always
leads to significant performance improvements. Conventional feature distillation framework …

Pruning convolutional filters has demonstrated its effectiveness in compressing ConvNets.
Prior art in filter pruning requires users to specify a target model complexity (eg, model size …

The rapid development and wide utilization of object detection techniques have aroused
attention on both accuracy and speed of object detectors. However, the current state-of-the …

This paper presents a survey of methods for pruning deep neural networks. It begins by
categorising over 150 studies based on the underlying approach used and then focuses on …