Matrix computation,as a fundamental building block of information processing in science and technology,contributes most of the computational overheads in modern signal processing and artificial intelligence algorithms...Matrix computation,as a fundamental building block of information processing in science and technology,contributes most of the computational overheads in modern signal processing and artificial intelligence algorithms.Photonic accelerators are designed to accelerate specific categories of computing in the optical domain,especially matrix multiplication,to address the growing demand for computing resources and capacity.Photonic matrix multiplication has much potential to expand the domain of telecommunication,and artificial intelligence benefiting from its superior performance.Recent research in photonic matrix multiplication has flourished and may provide opportunities to develop applications that are unachievable at present by conventional electronic processors.In this review,we first introduce the methods of photonic matrix multiplication,mainly including the plane light conversion method,Mach–Zehnder interferometer method and wavelength division multiplexing method.We also summarize the developmental milestones of photonic matrix multiplication and the related applications.Then,we review their detailed advances in applications to optical signal processing and artificial neural networks in recent years.Finally,we comment on the challenges and perspectives of photonic matrix multiplication and photonic acceleration.展开更多
Phase is a fundamental resource for optical imaging but cannot be directly observed with intensity measurements.The existing methods to quantify a phase distribution rely on complex devices and structures and lead to ...Phase is a fundamental resource for optical imaging but cannot be directly observed with intensity measurements.The existing methods to quantify a phase distribution rely on complex devices and structures and lead to difficulties of optical alignment and adjustment.We experimentally demonstrate a phase mining method based on the so-called adjustable spatial differentiation,by analyzing the polarization of light reflection from a single planar dielectric interface.Introducing an adjustable bias,we create a virtual light source to render the measured images with a shadow-cast effect.From the virtual shadowed images,we can further recover the phase distribution of a transparent object with the accuracy of 0.05λRMS.Without any dependence on wavelength or material dispersion,this method directly stems from the intrinsic properties of light and can be generally extended to a broad frequency range.展开更多
基金Chaoran Huang would like to thank Alexander Tait,Bhavin Shastri and Paul Prucnal for the fruitful discussions.J.J.D.acknowledges the support of the National Key Research and Development Project of China(2018YFB2201901)the National Natural Science Foundation of China(61805090,62075075).
文摘Matrix computation,as a fundamental building block of information processing in science and technology,contributes most of the computational overheads in modern signal processing and artificial intelligence algorithms.Photonic accelerators are designed to accelerate specific categories of computing in the optical domain,especially matrix multiplication,to address the growing demand for computing resources and capacity.Photonic matrix multiplication has much potential to expand the domain of telecommunication,and artificial intelligence benefiting from its superior performance.Recent research in photonic matrix multiplication has flourished and may provide opportunities to develop applications that are unachievable at present by conventional electronic processors.In this review,we first introduce the methods of photonic matrix multiplication,mainly including the plane light conversion method,Mach–Zehnder interferometer method and wavelength division multiplexing method.We also summarize the developmental milestones of photonic matrix multiplication and the related applications.Then,we review their detailed advances in applications to optical signal processing and artificial neural networks in recent years.Finally,we comment on the challenges and perspectives of photonic matrix multiplication and photonic acceleration.
基金The authors acknowledge funding through the National Natural Science Foundation of China(NSFC Grants Nos.91850108 and 61675179)the National Key Research and Development Program of China(Grant No.2017YFA0205700)the Open Foundation of the State Key Laboratory of Modern Optical Instrumentation,and the Open Research Program of Key Laboratory of 3D Micro/Nano Fabrication and Characterization of Zhejiang Province.Z.R.,T.Z.,and J.H.are named inventors on a number of related patent applications related to this work。
文摘Phase is a fundamental resource for optical imaging but cannot be directly observed with intensity measurements.The existing methods to quantify a phase distribution rely on complex devices and structures and lead to difficulties of optical alignment and adjustment.We experimentally demonstrate a phase mining method based on the so-called adjustable spatial differentiation,by analyzing the polarization of light reflection from a single planar dielectric interface.Introducing an adjustable bias,we create a virtual light source to render the measured images with a shadow-cast effect.From the virtual shadowed images,we can further recover the phase distribution of a transparent object with the accuracy of 0.05λRMS.Without any dependence on wavelength or material dispersion,this method directly stems from the intrinsic properties of light and can be generally extended to a broad frequency range.
