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 …

In recent years, convolutional neural networks (CNNs) have shown great performance in
various fields such as image classification, pattern recognition, and multi-media …

Neural network pruning---the task of reducing the size of a network by removing parameters--
-has been the subject of a great deal of work in recent years. We provide a meta-analysis of …

Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis proximity to where data are captured based on artificial …

Network quantization is essential for deploying deep models to IoT devices due to its high
efficiency. Most existing quantization approaches rely on the full training datasets and the …

Deep neural networks have evolved remarkably over the past few years and they are
currently the fundamental tools of many intelligent systems. At the same time, the …

Edge intelligence refers to a set of connected systems and devices for data collection,
caching, processing, and analysis in locations close to where data is captured based on …

Recent surge of Convolutional Neural Networks (CNNs) has brought successes among
various applications. However, these successes are accompanied by a significant increase …

Every bit matters in the hardware design of quantized neural networks. However, extremely-
low-bit representation usually causes large accuracy drop. Thus, how to train extremely-low …

Abstract Very large-scale Deep Neural Networks (DNNs) have achieved remarkable
successes in a large variety of computer vision tasks. However, the high computation …