Recent Advances in Convolutional Neural Network Acceleration

By Qianru Zhang, Meng Zhang, Tinghuan Chen, Zhifei Sun, Yuzhe Ma, Bei Yu. Neurocomputing, 2018

382 citations recorded by the source corpus.

Abstract In recent years, convolutional neural networks (CNNs) have shown great performance in various fields such as image classification, pattern recognition, and multi-media compression. Two of the feature properties, local connectivity and weight sharing, can reduce the number of parameters and increase processing speed during training and inference. However, as the dimension of data becomes higher and the CNN architecture becomes more complicated, the end-to-end approach or the combined manner of CNN is computationally intensive, which becomes limitation to CNN’s further implementation. Therefore, it is necessary and urgent to implement CNN in a faster way. In this paper, we first summarize the acceleration methods that contribute to but not limited to CNN by reviewing a broad variety of research papers. We propose a taxonomy in terms of three levels, i.e. structure level, algorithm level, and implementation level, for acceleration methods. We also analyze the acceleration methods in terms of CNN architecture compression, algorithm optimization, and hardware-based improvement. At last, we give a discussion on different perspectives of these acceleration and optimization methods within each level. The discussion shows that the methods in each level still have large exploration space. By incorporating such a wide range of disciplines, we expect to provide a comprehensive reference for researchers who are interested in CNN acceleration.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9552e625973a8c67a7e709cc4aa75c4fc71ce261

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Asifullah Khan
2. Anabia Sohail
3. Umme Zahoora
4. Aqsa Saeed Qureshi

In Computer Vision and Pattern Recognition

1. Asifullah Khan
2. Anabia Sohail
3. Umme Zahoora
4. Aqsa Saeed Qureshi

Citations in the corpus

• A survey of the recent architectures of deep convolutional neural networks (2019, background): “Likewise, the survey presented in (Zhang et al. 2019) discusses the taxonomy of CNNs based on acceleration techniques.”