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Recent advances in efficient computation of deep convolutional neural networks

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Abstract

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 computational complexity and resource consumption of these networks continue to increase. This poses a significant challenge to the deployment of such networks, especially in real-time applications or on resource-limited devices. Thus, network acceleration has become a hot topic within the deep learning community. As for hardware implementation of deep neural networks, a batch of accelerators based on a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC) have been proposed in recent years. In this paper, we provide a comprehensive survey of recent advances in network acceleration, compression, and accelerator design from both algorithm and hardware points of view. Specifically, we provide a thorough analysis of each of the following topics: network pruning, low-rank approximation, network quantization, teacher–student networks, compact network design, and hardware accelerators. Finally, we introduce and discuss a few possible future directions.

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Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Jian Cheng, Pei-song Wang, Gang Li, Qing-hao Hu & Han-qing Lu

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Jian Cheng, Pei-song Wang, Gang Li, Qing-hao Hu & Han-qing Lu

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  1. Jian Cheng
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Cheng, J., Wang, Ps., Li, G. et al. Recent advances in efficient computation of deep convolutional neural networks. Frontiers Inf Technol Electronic Eng 19, 64–77 (2018). https://doi.org/10.1631/FITEE.1700789

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