The rapid development of information technology has fueled an ever-increasing demand for ultrafast and ultralow-en-ergy-consumption computing.Existing computing instruments are pre-dominantly electronic processors,whi...The rapid development of information technology has fueled an ever-increasing demand for ultrafast and ultralow-en-ergy-consumption computing.Existing computing instruments are pre-dominantly electronic processors,which use elec-trons as information carriers and possess von Neumann architecture featured by physical separation of storage and pro-cessing.The scaling of computing speed is limited not only by data transfer between memory and processing units,but also by RC delay associated with integrated circuits.Moreover,excessive heating due to Ohmic losses is becoming a severe bottleneck for both speed and power consumption scaling.Using photons as information carriers is a promising alternative.Owing to the weak third-order optical nonlinearity of conventional materials,building integrated photonic com-puting chips under traditional von Neumann architecture has been a challenge.Here,we report a new all-optical comput-ing framework to realize ultrafast and ultralow-energy-consumption all-optical computing based on convolutional neural networks.The device is constructed from cascaded silicon Y-shaped waveguides with side-coupled silicon waveguide segments which we termed“weight modulators”to enable complete phase and amplitude control in each waveguide branch.The generic device concept can be used for equation solving,multifunctional logic operations as well as many other mathematical operations.Multiple computing functions including transcendental equation solvers,multifarious logic gate operators,and half-adders were experimentally demonstrated to validate the all-optical computing performances.The time-of-flight of light through the network structure corresponds to an ultrafast computing time of the order of several picoseconds with an ultralow energy consumption of dozens of femtojoules per bit.Our approach can be further expan-ded to fulfill other complex computing tasks based on non-von Neumann architectures and thus paves a new way for on-chip all-optical computing.展开更多
针对布匹瑕疵自动化检测,基于传统的机器视觉方法依赖于人工设计特征,对具有复杂背景图案的花色布瑕疵特征提取难度非常大,因此提出一种基于改进Faster R-CNN(faster region with convolutional neural network)的花色布瑕疵检测算法。...针对布匹瑕疵自动化检测,基于传统的机器视觉方法依赖于人工设计特征,对具有复杂背景图案的花色布瑕疵特征提取难度非常大,因此提出一种基于改进Faster R-CNN(faster region with convolutional neural network)的花色布瑕疵检测算法。在Faster R-CNN的基础上使用Resnet-50作为主干网络,嵌入可变形卷积来提高瑕疵特征的学习能力。通过设计多尺度模型来提高小瑕疵的检测,引入级联网络来提高瑕疵检测精度和定位准确度,构造优化的损失函数来降低样本不平衡影响。通过试验验证了该算法的有效性。结果表明,瑕疵检测效果准确率达94.97%,并能精准定位瑕疵位置,可满足工厂的实际需求。展开更多
基金financial supports from the National Key Research and Development Program of China(2018YFB2200403)National Natural Sci-ence Foundation of China(NSFC)(61775003,11734001,91950204,11527901,11604378,91850117).
文摘The rapid development of information technology has fueled an ever-increasing demand for ultrafast and ultralow-en-ergy-consumption computing.Existing computing instruments are pre-dominantly electronic processors,which use elec-trons as information carriers and possess von Neumann architecture featured by physical separation of storage and pro-cessing.The scaling of computing speed is limited not only by data transfer between memory and processing units,but also by RC delay associated with integrated circuits.Moreover,excessive heating due to Ohmic losses is becoming a severe bottleneck for both speed and power consumption scaling.Using photons as information carriers is a promising alternative.Owing to the weak third-order optical nonlinearity of conventional materials,building integrated photonic com-puting chips under traditional von Neumann architecture has been a challenge.Here,we report a new all-optical comput-ing framework to realize ultrafast and ultralow-energy-consumption all-optical computing based on convolutional neural networks.The device is constructed from cascaded silicon Y-shaped waveguides with side-coupled silicon waveguide segments which we termed“weight modulators”to enable complete phase and amplitude control in each waveguide branch.The generic device concept can be used for equation solving,multifunctional logic operations as well as many other mathematical operations.Multiple computing functions including transcendental equation solvers,multifarious logic gate operators,and half-adders were experimentally demonstrated to validate the all-optical computing performances.The time-of-flight of light through the network structure corresponds to an ultrafast computing time of the order of several picoseconds with an ultralow energy consumption of dozens of femtojoules per bit.Our approach can be further expan-ded to fulfill other complex computing tasks based on non-von Neumann architectures and thus paves a new way for on-chip all-optical computing.
文摘针对布匹瑕疵自动化检测,基于传统的机器视觉方法依赖于人工设计特征,对具有复杂背景图案的花色布瑕疵特征提取难度非常大,因此提出一种基于改进Faster R-CNN(faster region with convolutional neural network)的花色布瑕疵检测算法。在Faster R-CNN的基础上使用Resnet-50作为主干网络,嵌入可变形卷积来提高瑕疵特征的学习能力。通过设计多尺度模型来提高小瑕疵的检测,引入级联网络来提高瑕疵检测精度和定位准确度,构造优化的损失函数来降低样本不平衡影响。通过试验验证了该算法的有效性。结果表明,瑕疵检测效果准确率达94.97%,并能精准定位瑕疵位置,可满足工厂的实际需求。
