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[ "闵锐(1978- ),男,中国电信股份有限公司智能网络与终端研究院高级工程师,主要研究方向为大数据、图像识别及人工智能" ]
网络出版日期:2020-04,
纸质出版日期:2020-04-20
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闵锐. 高效深度神经网络综述[J]. 电信科学, 2020,36(4):115-124.
Rui MIN. A survey of efficient deep neural network[J]. Telecommunications science, 2020, 36(4): 115-124.
闵锐. 高效深度神经网络综述[J]. 电信科学, 2020,36(4):115-124. DOI: 10.11959/j.issn.1000-0801.2020119.
Rui MIN. A survey of efficient deep neural network[J]. Telecommunications science, 2020, 36(4): 115-124. DOI: 10.11959/j.issn.1000-0801.2020119.
近年来,深度神经网络(DNN)在计算机视觉、自然语言处理等AI领域中取得了巨大的成功。得益于更深更大的网络结构,DNN的性能正在迅速提升。然而,更深更大的深度神经网络需要巨大的计算和内存资源,在资源受限的场景中,很难部署较大的神经网络模型。如何设计轻量并且高效的深度神经网络来加速其在嵌入式设备上的运行速度,对于推进深度神经网络技术的落地意义巨大。对近年来具有代表性的高效深度神经网络的研究方法和工作进行回顾和总结,包括参数剪枝、模型量化、知识蒸馏、网络搜索和量化。同时分析了不同方法的优点和缺点以及适用场景,并且展望了高效神经网络设计的发展趋势。
Recently
deep neural network (DNN) has achieved great success in the field of AI such as computer vision and natural language processing.Thanks to a deeper and larger network structure
DNN’s performance is rapidly increasing.However
deeper and lager deep neural networks require huge computational and memory resources.In some resource-constrained scenarios
it is difficult to deploy large neural network models.How to design a lightweight and efficient deep neural network to accelerate its running speed on embedded devices is a great research hotspot for advancing deep neural network technology.The research methods and work of representative high-efficiency deep neural networks in recent years were reviewed and summarized
including parameter pruning
model quantification
knowledge distillation
network search and quantification.Also
vadvantages and disadvantages of different methods as well as applicable scenarios were analyzed
and the future development trend of efficient neural network design was forecasted.
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