The rapidly growing computational demands of artificial intelligence(AI)and complex optimization tasks are increasingly straining conventional electronic architectures,driving the search for novel,energy-efficient pro...The rapidly growing computational demands of artificial intelligence(AI)and complex optimization tasks are increasingly straining conventional electronic architectures,driving the search for novel,energy-efficient processing paradigms.Photonic computing,which harnesses the unique properties of light to perform computation,has emerged as a compelling alternative.This perspective highlights a key advancement:a versatile nonlinear optoelectronic engine based on integrated photodetectors and micro-ring modulators(PD+MRM).This engine enables crucial functionalities like nonlinear activation and signal relay,forming a core building block for monolithic photonic processors.Its application in integrating optical Ising machines for optimization and optical recurrent neural networks(RNNs)for AI has been examined recently.The PD+MRM unit’s inherent compactness,efficiency,and onchip reconfigurable nonlinearity address historical photonic computing challenges,signaling a shift towards more versatile and scalable monolithic photonic processors.展开更多
文摘The rapidly growing computational demands of artificial intelligence(AI)and complex optimization tasks are increasingly straining conventional electronic architectures,driving the search for novel,energy-efficient processing paradigms.Photonic computing,which harnesses the unique properties of light to perform computation,has emerged as a compelling alternative.This perspective highlights a key advancement:a versatile nonlinear optoelectronic engine based on integrated photodetectors and micro-ring modulators(PD+MRM).This engine enables crucial functionalities like nonlinear activation and signal relay,forming a core building block for monolithic photonic processors.Its application in integrating optical Ising machines for optimization and optical recurrent neural networks(RNNs)for AI has been examined recently.The PD+MRM unit’s inherent compactness,efficiency,and onchip reconfigurable nonlinearity address historical photonic computing challenges,signaling a shift towards more versatile and scalable monolithic photonic processors.
