The availability of computers and communication networks allows us to gather and analyse data on a far larger scale than previously. At present, it is believed that statistics is a suitable method to analyse networks ...The availability of computers and communication networks allows us to gather and analyse data on a far larger scale than previously. At present, it is believed that statistics is a suitable method to analyse networks with millions, or more, of vertices. The MATLAB language, with its mass of statistical functions, is a good choice to rapidly realize an algorithm prototype of complex networks. The performance of the MATLAB codes can be further improved by using graphic processor units （GPU）. This paper presents the strategies and performance of the GPU implementation of a complex networks package, and the Jacket toolbox of MATLAB is used. Compared with some commercially available CPU implementations, GPU can achieve a speedup of, on average, 11.3x. The experimental result proves that the GPU platform combined with the MATLAB language is a good combination for complex network research.展开更多
A detailed experiment of 1-pixel bit reconfigurable ternary optical processor （TOP） is proposed in the paper. 42 basic operation units （BOUs） and 28 typical logic operators of the TOP are realized in the experimen...A detailed experiment of 1-pixel bit reconfigurable ternary optical processor （TOP） is proposed in the paper. 42 basic operation units （BOUs） and 28 typical logic operators of the TOP are realized in the experiment. Results of the test cases elaborately cover the every combination of BOUs and all the nine inputs of ternary processor. Both the experiment process and results analysis are given in this paper. The experimental results demonstrate that the theory of reconfiguring a TOP is valid and that the reconfiguration circuitry is effective.展开更多
To address the current issues of low reconfigurability,low integration,and high dynamic power consumption in programmable units,this study proposes a novel programmable photonic unit cell,termed MZI-cascaded-ring unit...To address the current issues of low reconfigurability,low integration,and high dynamic power consumption in programmable units,this study proposes a novel programmable photonic unit cell,termed MZI-cascaded-ring unit(MCR).The unit functions analogously to an MZI,enabling broadband routing when operating within the free spectral range(FSR)of the embedded resonator,and it transitions into a wavelength-selective mode,leveraging the micro-ring’s resonance to achieve precise amplitude and phase control for narrowband signals while outside the FSR,featuring dual operational regimes.With the implementation of spiral waveguide structures,the design achieves higher integration density and lower dynamic power consumption.Based on the hexagonal mesh extension of such a unit,the programmable photonic processor successfully demonstrates a reconfiguration of large amounts of fundamental functions with tunable performance metrics,including broadband linear operations like optical router and wavelength-selective functionalities like wavelength division multiplexing.This work establishes a new paradigm for programmable photonic integrated circuit design.展开更多
This paper presents the application of a novel AI-based approach,Neural Physics,to produce high-fidelity simulations of train aerodynamics.Neural Physics is built upon convolutional neural networks(CNNs),where the wei...This paper presents the application of a novel AI-based approach,Neural Physics,to produce high-fidelity simulations of train aerodynamics.Neural Physics is built upon convolutional neural networks(CNNs),where the weights are explicitly determined by classical numerical discretisation schemes rather than by training.By leveraging the power of AI technology,this recent approach results in code that can run easily on GPUs and AI processors,achieving high computational speed without sacrificing accuracy.The approach uses an implicit large eddy simulation method based on a non-linear Petrov-Galerkin method to model the unresolved turbulence.Furthermore,for higher-order finite elements,the convolutional finite element method(ConvFEM)is used,which greatly simplifies the implementation of higher-order elements within the NN 4 DPEs approach.We demonstrate the capability of Neural Physics by simulating a freight Locomotive Class 66 and a partially loaded freight train operating in an open field environment with and without cross wind.This is the first time that ConvFEM has been applied to high-speed fluid flow problems in complex geometries.The results are validated against existing numerical results and experimental measurements,and show good agreement in terms of pressure and velocity distributions around the train body.展开更多
基金Project supported by the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (Grant No.60921062)the National Natural Science Foundation of China (Grant No.60873014)the Young Scientists Fund of the National Natural Science Foundation of China (Grant Nos.61003082 and 60903059)
文摘The availability of computers and communication networks allows us to gather and analyse data on a far larger scale than previously. At present, it is believed that statistics is a suitable method to analyse networks with millions, or more, of vertices. The MATLAB language, with its mass of statistical functions, is a good choice to rapidly realize an algorithm prototype of complex networks. The performance of the MATLAB codes can be further improved by using graphic processor units （GPU）. This paper presents the strategies and performance of the GPU implementation of a complex networks package, and the Jacket toolbox of MATLAB is used. Compared with some commercially available CPU implementations, GPU can achieve a speedup of, on average, 11.3x. The experimental result proves that the GPU platform combined with the MATLAB language is a good combination for complex network research.
基金Project supported by the National Natural Science Foundation of China(Grant No.61073049)the Shanghai Leading Academic Discipline Project(Grant No.J50103)the Doctorate Foundation of Education Ministry of China(Grant No.20093108110016)
文摘A detailed experiment of 1-pixel bit reconfigurable ternary optical processor （TOP） is proposed in the paper. 42 basic operation units （BOUs） and 28 typical logic operators of the TOP are realized in the experiment. Results of the test cases elaborately cover the every combination of BOUs and all the nine inputs of ternary processor. Both the experiment process and results analysis are given in this paper. The experimental results demonstrate that the theory of reconfiguring a TOP is valid and that the reconfiguration circuitry is effective.
基金National Natural Science Foundation of China(62075038)Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX24_0401).
文摘To address the current issues of low reconfigurability,low integration,and high dynamic power consumption in programmable units,this study proposes a novel programmable photonic unit cell,termed MZI-cascaded-ring unit(MCR).The unit functions analogously to an MZI,enabling broadband routing when operating within the free spectral range(FSR)of the embedded resonator,and it transitions into a wavelength-selective mode,leveraging the micro-ring’s resonance to achieve precise amplitude and phase control for narrowband signals while outside the FSR,featuring dual operational regimes.With the implementation of spiral waveguide structures,the design achieves higher integration density and lower dynamic power consumption.Based on the hexagonal mesh extension of such a unit,the programmable photonic processor successfully demonstrates a reconfiguration of large amounts of fundamental functions with tunable performance metrics,including broadband linear operations like optical router and wavelength-selective functionalities like wavelength division multiplexing.This work establishes a new paradigm for programmable photonic integrated circuit design.
基金Projects(EPSRC EP/Y005732/1,EP/Y018680/1,EP/T003189/1,EP/V040235/1,EP/Y024257/1 and EP/T000414/1)supported by UK Research and Innovation(UKRI),UKProject(APP44894/UKRI 1281)supported by UKRI councils(NERC,AHRC,ESRC,MRC and DEFRA),UK。
文摘This paper presents the application of a novel AI-based approach,Neural Physics,to produce high-fidelity simulations of train aerodynamics.Neural Physics is built upon convolutional neural networks(CNNs),where the weights are explicitly determined by classical numerical discretisation schemes rather than by training.By leveraging the power of AI technology,this recent approach results in code that can run easily on GPUs and AI processors,achieving high computational speed without sacrificing accuracy.The approach uses an implicit large eddy simulation method based on a non-linear Petrov-Galerkin method to model the unresolved turbulence.Furthermore,for higher-order finite elements,the convolutional finite element method(ConvFEM)is used,which greatly simplifies the implementation of higher-order elements within the NN 4 DPEs approach.We demonstrate the capability of Neural Physics by simulating a freight Locomotive Class 66 and a partially loaded freight train operating in an open field environment with and without cross wind.This is the first time that ConvFEM has been applied to high-speed fluid flow problems in complex geometries.The results are validated against existing numerical results and experimental measurements,and show good agreement in terms of pressure and velocity distributions around the train body.
