The explosive growth of data and information has motivated various emerging non-von Neumann computational approaches in the More-than-Moore era.Photonics neuromorphic computing has attracted lots of attention due to t...The explosive growth of data and information has motivated various emerging non-von Neumann computational approaches in the More-than-Moore era.Photonics neuromorphic computing has attracted lots of attention due to the fascinating advantages such as high speed,wide bandwidth,and massive parallelism.Here,we offer a review on the optical neural computing in our research groups at the device and system levels.The photonics neuron and photonics synapse plasticity are presented.In addition,we introduce several optical neural computing architectures and algorithms including photonic spiking neural network,photonic convolutional neural network,photonic matrix computation,photonic reservoir computing,and photonic reinforcement learning.Finally,we summarize the major challenges faced by photonic neuromorphic computing,and propose promising solutions and perspectives.展开更多
We propose and demonstrate experimentally and numerically a network of three globally coupled semiconductor lasers(SLs)that generate triple-channel chaotic signals with time delayed signature(TDS)concealment.The effec...We propose and demonstrate experimentally and numerically a network of three globally coupled semiconductor lasers(SLs)that generate triple-channel chaotic signals with time delayed signature(TDS)concealment.The effects of the coupling strength and bias current on the concealment of the TDS are investigated.The generated chaotic signals are further applied to reinforcement learning,and a parallel scheme is proposed to solve the multiarmed bandit(MAB)problem.The influences of mutual correlation between signals from different channels,the sampling interval of signals,and the TDS concealment on the performance of decision making are analyzed.Comparisons between the proposed scheme and two existing schemes show that,with a simplified algorithm,the proposed scheme can perform as well as the previous schemes or even better.Moreover,we also consider the robustness of decision making performance against a dynamically changing environment and verify the scalability for MAB problems with different sizes.This proposed globally coupled SL network for a multi-channel chaotic source is simple in structure and easy to implement.The attempt to solve the MAB problem in parallel can provide potential values in the realm of the application of ultrafast photonics intelligence.展开更多
基金This work was supported in part by the National Outstanding Youth Science Fund Project of National Natural Science Foundation of China(62022062)the National Natural Science Foundation of China(61974177,61674119)the Fundamental Research Funds for the Central Universities.
文摘The explosive growth of data and information has motivated various emerging non-von Neumann computational approaches in the More-than-Moore era.Photonics neuromorphic computing has attracted lots of attention due to the fascinating advantages such as high speed,wide bandwidth,and massive parallelism.Here,we offer a review on the optical neural computing in our research groups at the device and system levels.The photonics neuron and photonics synapse plasticity are presented.In addition,we introduce several optical neural computing architectures and algorithms including photonic spiking neural network,photonic convolutional neural network,photonic matrix computation,photonic reservoir computing,and photonic reinforcement learning.Finally,we summarize the major challenges faced by photonic neuromorphic computing,and propose promising solutions and perspectives.
基金National Natural Science Foundation of China(61974177,61674119).
文摘We propose and demonstrate experimentally and numerically a network of three globally coupled semiconductor lasers(SLs)that generate triple-channel chaotic signals with time delayed signature(TDS)concealment.The effects of the coupling strength and bias current on the concealment of the TDS are investigated.The generated chaotic signals are further applied to reinforcement learning,and a parallel scheme is proposed to solve the multiarmed bandit(MAB)problem.The influences of mutual correlation between signals from different channels,the sampling interval of signals,and the TDS concealment on the performance of decision making are analyzed.Comparisons between the proposed scheme and two existing schemes show that,with a simplified algorithm,the proposed scheme can perform as well as the previous schemes or even better.Moreover,we also consider the robustness of decision making performance against a dynamically changing environment and verify the scalability for MAB problems with different sizes.This proposed globally coupled SL network for a multi-channel chaotic source is simple in structure and easy to implement.The attempt to solve the MAB problem in parallel can provide potential values in the realm of the application of ultrafast photonics intelligence.
