Recent advances in efficient computation of deep convolutional neural networks
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 dep...
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Published in | Frontiers of information technology & electronic engineering Vol. 19; no. 1; pp. 64 - 77 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Hangzhou
Zhejiang University Press
2018
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Summary: | 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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ISSN: | 2095-9184 2095-9230 |
DOI: | 10.1631/FITEE.1700789 |