Photonic computing has emerged as a promising technology for the ever-increasing computational demands of machine learning and artificial intelligence.Due to the advantages in computing speed,integrated photonic chips...Photonic computing has emerged as a promising technology for the ever-increasing computational demands of machine learning and artificial intelligence.Due to the advantages in computing speed,integrated photonic chips have attracted wide research attention on performing convolutional neural network algorithm.Programmable photonic chips are vital for achieving practical applications of photonic computing.Herein,a programmable photonic chip based on ultrafast laser-induced phase change is fabricated for photonic computing.Through designing the ultrafast laser pulses,the Sb film integrated into photonic waveguides can be reversibly switched between crystalline and amorphous phase,resulting in a large contrast in refractive index and extinction coefficient.As a consequence,the light transmission of waveguides can be switched between write and erase states.To determine the phase change time,the transient laser-induced phase change dynamics of Sb film are revealed at atomic scale,and the time-resolved transient reflectivity is measured.Based on the integrated photonic chip,photonic convolutional neural networks are built to implement machine learning algorithm,and images recognition task is achieved.This work paves a route for fabricating programmable photonic chips by designed ultrafast laser,which will facilitate the application of photonic computing in artificial intelligence.展开更多
The single event effects(SEEs)evaluations caused by atmospheric neutrons were conducted on three different convolutional neural network(CNN)models(Yolov3,MNIST,and ResNet50)in the atmospheric neutron irradiation spect...The single event effects(SEEs)evaluations caused by atmospheric neutrons were conducted on three different convolutional neural network(CNN)models(Yolov3,MNIST,and ResNet50)in the atmospheric neutron irradiation spectrometer(ANIS)at the China Spallation Neutron Source(CSNS).The Yolov3 and MNIST models were implemented on the XILINX28-nm system-on-chip(So C).Meanwhile,the Yolov3 and ResNet50 models were deployed on the XILINX 16-nm Fin FET Ultra Scale+MPSoC.The atmospheric neutron SEEs on the tested CNN systems were comprehensively evaluated from six aspects,including chip type,network architecture,deployment methods,inference time,datasets,and the position of the anchor boxes.The various types of SEE soft errors,SEE cross-sections,and their distribution were analyzed to explore the radiation sensitivities and rules of 28-nm and 16-nm SoC.The current research can provide the technology support of radiation-resistant design of CNN system for developing and applying high-reliability,long-lifespan domestic artificial intelligence chips.展开更多
在储能系统实际运行中,准确评估电池的荷电状态(State of Charge, SOC)是确保系统高效、安全运行的关键。为此,在对现有锂电池等效电路模型及参数辨识方法进行综述的基础上,提出了一种基于戴维南改进模型的创新的锂电池SOC仿真研究方法...在储能系统实际运行中,准确评估电池的荷电状态(State of Charge, SOC)是确保系统高效、安全运行的关键。为此,在对现有锂电池等效电路模型及参数辨识方法进行综述的基础上,提出了一种基于戴维南改进模型的创新的锂电池SOC仿真研究方法。通过深入研究并网储能系统的拓扑结构与控制策略,构建了细致且精确的数学模型,并运用MATLAB仿真软件进行了建模与分析。实验仿真结果表明,该改进模型能够高效、准确地模拟锂电池SOC的动态变化,为储能系统的优化设计与运行控制提供了理论支持,对于提升储能系统的整体性能具有重要意义。展开更多
针对光伏发电应用领域太阳能路灯系统的过充电或过放电现象对蓄电池本身特性产生影响、降低使用寿命的问题,采用单片机和LabVIEW进行太阳能路灯蓄电池电压检测,采用BP神经网络进行太阳能路灯蓄电池荷电率(SOC)预测。BP神经网络将测得数...针对光伏发电应用领域太阳能路灯系统的过充电或过放电现象对蓄电池本身特性产生影响、降低使用寿命的问题,采用单片机和LabVIEW进行太阳能路灯蓄电池电压检测,采用BP神经网络进行太阳能路灯蓄电池荷电率(SOC)预测。BP神经网络将测得数据建立SOC(State of Charge)预测模型,LabVIEW可视化面板实时显示测量数据、波形及预测结果,实现太阳能路灯智能化控制。测试结果表明,系统能够实时检测蓄电池充电电压,并预测电池工作状态,BP神经网络蓄电池SOC预测值与蓄电池电量实测误差为0.1%~0.4%,满足网络误差要求。展开更多
随着以太网技术和集成电路技术的发展,以太网物理层(Physical Layer,PHY)芯片的速率和性能都得到了极大提升,电路复杂度更是几何级增长,以至于常规的自动测试设备(Automatic Test Equipment,ATE)测试很难充分验证其功能,所以亟需开展相...随着以太网技术和集成电路技术的发展,以太网物理层(Physical Layer,PHY)芯片的速率和性能都得到了极大提升,电路复杂度更是几何级增长,以至于常规的自动测试设备(Automatic Test Equipment,ATE)测试很难充分验证其功能,所以亟需开展相应测试方法研究。提出了一种高效的基于ZYNQ MPSOC的以太网PHY芯片功能测试方法。该方法以ZYNQ MPSOC为核心,设计了一种直达应用层面的系统级测试装置,从而减少了与物理层直接交互的行为,有效降低了测试装置及程序开发难度。经试验验证,提出的基于ZYNQ MPSOC的以太网PHY芯片功能测试方法能够用于以太网PHY芯片测试。展开更多
基金supported by the National Key R&D Program of China(2024YFB4609801)the National Natural Science Foundation of China(52075289)the Tsinghua-Jiangyin Innovation Special Fund(TJISF,2023JYTH0104).
文摘Photonic computing has emerged as a promising technology for the ever-increasing computational demands of machine learning and artificial intelligence.Due to the advantages in computing speed,integrated photonic chips have attracted wide research attention on performing convolutional neural network algorithm.Programmable photonic chips are vital for achieving practical applications of photonic computing.Herein,a programmable photonic chip based on ultrafast laser-induced phase change is fabricated for photonic computing.Through designing the ultrafast laser pulses,the Sb film integrated into photonic waveguides can be reversibly switched between crystalline and amorphous phase,resulting in a large contrast in refractive index and extinction coefficient.As a consequence,the light transmission of waveguides can be switched between write and erase states.To determine the phase change time,the transient laser-induced phase change dynamics of Sb film are revealed at atomic scale,and the time-resolved transient reflectivity is measured.Based on the integrated photonic chip,photonic convolutional neural networks are built to implement machine learning algorithm,and images recognition task is achieved.This work paves a route for fabricating programmable photonic chips by designed ultrafast laser,which will facilitate the application of photonic computing in artificial intelligence.
基金Project supported by the National Natural Science Foundation of China(Grant No.12305303)the Natural Science Foundation of Hunan Province of China(Grant Nos.2023JJ40520,2024JJ2044,and 2021JJ40444)+3 种基金the Science and Technology Innovation Program of Hunan Province,China(Grant No.2020RC3054)the Postgraduate Scientific Research Innovation Project of Hunan Province,China(Grant No.CX20240831)the Natural Science Basic Research Plan in the Shaanxi Province of China(Grant No.2023-JC-QN0015)the Doctoral Research Fund of University of South China(Grant No.200XQD033)。
文摘The single event effects(SEEs)evaluations caused by atmospheric neutrons were conducted on three different convolutional neural network(CNN)models(Yolov3,MNIST,and ResNet50)in the atmospheric neutron irradiation spectrometer(ANIS)at the China Spallation Neutron Source(CSNS).The Yolov3 and MNIST models were implemented on the XILINX28-nm system-on-chip(So C).Meanwhile,the Yolov3 and ResNet50 models were deployed on the XILINX 16-nm Fin FET Ultra Scale+MPSoC.The atmospheric neutron SEEs on the tested CNN systems were comprehensively evaluated from six aspects,including chip type,network architecture,deployment methods,inference time,datasets,and the position of the anchor boxes.The various types of SEE soft errors,SEE cross-sections,and their distribution were analyzed to explore the radiation sensitivities and rules of 28-nm and 16-nm SoC.The current research can provide the technology support of radiation-resistant design of CNN system for developing and applying high-reliability,long-lifespan domestic artificial intelligence chips.
文摘在储能系统实际运行中,准确评估电池的荷电状态(State of Charge, SOC)是确保系统高效、安全运行的关键。为此,在对现有锂电池等效电路模型及参数辨识方法进行综述的基础上,提出了一种基于戴维南改进模型的创新的锂电池SOC仿真研究方法。通过深入研究并网储能系统的拓扑结构与控制策略,构建了细致且精确的数学模型,并运用MATLAB仿真软件进行了建模与分析。实验仿真结果表明,该改进模型能够高效、准确地模拟锂电池SOC的动态变化,为储能系统的优化设计与运行控制提供了理论支持,对于提升储能系统的整体性能具有重要意义。
文摘针对光伏发电应用领域太阳能路灯系统的过充电或过放电现象对蓄电池本身特性产生影响、降低使用寿命的问题,采用单片机和LabVIEW进行太阳能路灯蓄电池电压检测,采用BP神经网络进行太阳能路灯蓄电池荷电率(SOC)预测。BP神经网络将测得数据建立SOC(State of Charge)预测模型,LabVIEW可视化面板实时显示测量数据、波形及预测结果,实现太阳能路灯智能化控制。测试结果表明,系统能够实时检测蓄电池充电电压,并预测电池工作状态,BP神经网络蓄电池SOC预测值与蓄电池电量实测误差为0.1%~0.4%,满足网络误差要求。
