针对遗传算法在理论研究方面存在的不足 ,系统地讨论了遗传算法理论研究的主要内容和方法 ,包括模式定理、编码策略、Markov链与全局收敛性、维数分析、BGA理论、可分离函数、Walsh与傅立叶函数分析及二次动力系统等 ,介绍了 No Free L ...针对遗传算法在理论研究方面存在的不足 ,系统地讨论了遗传算法理论研究的主要内容和方法 ,包括模式定理、编码策略、Markov链与全局收敛性、维数分析、BGA理论、可分离函数、Walsh与傅立叶函数分析及二次动力系统等 ,介绍了 No Free L unch定理 。展开更多
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 t...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.展开更多
文摘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.
