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 a modified supervised learning algorithm for optical spiking neural networks,which introduces synaptic time-delay plasticity on the basis of traditional weight training.Delay learning is combined with the r...We propose a modified supervised learning algorithm for optical spiking neural networks,which introduces synaptic time-delay plasticity on the basis of traditional weight training.Delay learning is combined with the remote supervised method that is incorporated with photonic spike-timing-dependent plasticity.A spike sequence learning task implemented via the proposed algorithm is found to have better performance than via the traditional weight-based method.Moreover,the proposed algorithm is also applied to two benchmark data sets for classification.In a simple network structure with only a few optical neurons,the classification accuracy based on the delay-weight learning algorithm is significantly improved compared with weight-based learning.The introduction of delay adjusting improves the learning efficiency and performance of the algorithm,which is helpful for photonic neuromorphic computing and is also important specifically for understanding information processing in the biological brain.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 Outstanding Youth Science Fund Project of National Natural Science Foundation of China(62022062)National Natural Science Foundation of China(61674119,61974177).
文摘We propose a modified supervised learning algorithm for optical spiking neural networks,which introduces synaptic time-delay plasticity on the basis of traditional weight training.Delay learning is combined with the remote supervised method that is incorporated with photonic spike-timing-dependent plasticity.A spike sequence learning task implemented via the proposed algorithm is found to have better performance than via the traditional weight-based method.Moreover,the proposed algorithm is also applied to two benchmark data sets for classification.In a simple network structure with only a few optical neurons,the classification accuracy based on the delay-weight learning algorithm is significantly improved compared with weight-based learning.The introduction of delay adjusting improves the learning efficiency and performance of the algorithm,which is helpful for photonic neuromorphic computing and is also important specifically for understanding information processing in the biological brain.
