Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture.Linear weighting and nonlinear spike activation are two fundamental functions...Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture.Linear weighting and nonlinear spike activation are two fundamental functions of a photonic spiking neural network(PSNN).However,they are separately implemented with different photonic materials and devices,hindering the large-scale integration of PSNN.Here,we propose,fabricate and experimentally demonstrate a photonic neuro-synaptic chip enabling the simultaneous implementation of linear weighting and nonlinear spike activation based on a distributed feedback(DFB)laser with a saturable absorber(DFB-SA).A prototypical system is experimentally constructed to demonstrate the parallel weighted function and nonlinear spike activation.Furthermore,a fourchannel DFB-SA laser array is fabricated for realizing matrix convolution of a spiking convolutional neural network,achieving a recognition accuracy of 87%for the MNIST dataset.The fabricated neuro-synaptic chip offers a fundamental building block to construct the large-scale integrated PSNN chip.展开更多
Spiking neural networks(SNNs)utilize brain-like spatiotemporal spike encoding for simulating brain functions.Photonic SNN offers an ultrahigh speed and power efficiency platform for implementing high-performance neuro...Spiking neural networks(SNNs)utilize brain-like spatiotemporal spike encoding for simulating brain functions.Photonic SNN offers an ultrahigh speed and power efficiency platform for implementing high-performance neuromorphic computing.Here,we proposed a multi-synaptic photonic SNN,combining the modified remote supervised learning with delayweight co-training to achieve pattern classification.The impact of multi-synaptic connections and the robustness of the network were investigated through numerical simulations.In addition,the collaborative computing of algorithm and hardware was demonstrated based on a fabricated integrated distributed feedback laser with a saturable absorber(DFB-SA),where 10 different noisy digital patterns were successfully classified.A functional photonic SNN that far exceeds the scale limit of hardware integration was achieved based on time-division multiplexing,demonstrating the capability of hardware-algorithm co-computation.展开更多
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
We proposed and experimentally demonstrated a simple and novel photonic spiking neuron based on a distributed feedback(DFB)laser chip with an intracavity saturable absorber(SA).The DFB laser with an intracavity SA(DFB...We proposed and experimentally demonstrated a simple and novel photonic spiking neuron based on a distributed feedback(DFB)laser chip with an intracavity saturable absorber(SA).The DFB laser with an intracavity SA(DFBSA)contains a gain region and an SA region.The gain region is designed and fabricated by the asymmetric equivalentπ-phase shift based on the reconstruction-equivalent-chirp technique.Under properly injected current in the gain region and reversely biased voltage in the SA region,periodic self-pulsation was experimentally observed due to the Q-switching effect.The self-pulsation frequency increases with the increase of the bias current and is within the range of several gigahertz.When the bias current is below the self-pulsation threshold,neuronlike spiking responses appear when external optical stimulus pulses are injected.Experimental results show that the spike threshold,temporal integration,and refractory period can all be observed in the fabricated DFB-SA chip.To numerically verify the experimental findings,a time-dependent coupled-wave equation model was developed,which described the physics processes inside the gain and SA regions.The numerical results agree well with the experimental measurements.We further experimentally demonstrated that the weighted sum output can readily be encoded into the self-pulsation frequency of the DFB-SA neuron.We also benchmarked the handwritten digit classification task with a simple single-layer fully connected neural network.By using the experimentally measured dependence of the self-pulsation frequency on the bias current in the gain region as an activation function,we can achieve a recognition accuracy of 92.2%,which bridges the gap between the continuous valued artificial neural networks and spike-based neuromorphic networks.To the best of our knowledge,this is the first experimental demonstration of a photonic integrated spiking neuron based on a DFB-SA,which shows great potential to realizing large-scale multiwavelength photonic spiking neural network chips.展开更多
Dendrites,branches of neurons that transmit signals between synapses and soma,play a vital role in spiking information processing,such as nonlinear integration of excitatory and inhibitory stimuli.However,the investig...Dendrites,branches of neurons that transmit signals between synapses and soma,play a vital role in spiking information processing,such as nonlinear integration of excitatory and inhibitory stimuli.However,the investigation of nonlinear integration of dendrites in photonic neurons and the fabrication of photonic neurons including dendritic nonlinear integration in photonic spiking neural networks(SNNs)remain open problems.Here,we fabricate and integrate two dendrites and one soma in a single Fabry–Perot laser with an embedded saturable absorber(FP-SA)neuron to achieve nonlinear integration of excitatory and inhibitory stimuli.Note that the two intrinsic electrodes of the gain section and saturable absorber(SA)section in the FP-SA neuron are defined as two dendrites for two ports of stimuli reception,with one electronic dendrite receiving excitatory stimulus and the other receiving inhibitory stimulus.The stimuli received by two electronic dendrites are integrated non-linearly in a single FP-SA neuron,which generates spikes for photonic SNNs.The properties of frequency encoding and spatiotemporal encoding are investigated experimentally in a single FP-SA neuron with two electronic dendrites.For SNNs equipped with FP-SA neurons,the range of weights between presynaptic neurons and postsynaptic neurons is varied from negative to positive values by biasing the gain and SA sections of FP-SA neurons.Compared with SNN with all-positive weights realized by only biasing the gain section of photonic neurons,the recognition accuracy of Iris flower data is improved numerically in SNN consisting of FP-SA neurons.The results show great potential for multi-functional integrated photonic SNN chips.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
S-scheme possesses superior redox capabilities compared with the II-scheme,providing an effective method to solve the innate defects of g-C_(3)N_(4)(CN).In this study,S-doped g-C_(3)N_(4)/g-C_(3)N_(4)(SCN-tm/CN)S-sche...S-scheme possesses superior redox capabilities compared with the II-scheme,providing an effective method to solve the innate defects of g-C_(3)N_(4)(CN).In this study,S-doped g-C_(3)N_(4)/g-C_(3)N_(4)(SCN-tm/CN)S-scheme homojunction was constructed by rationally integrating morphology control with interfacial engineering to enhance the photocatalytic hydrogen evolution performance.In-situ Kelvin probe force microscopy(KPFM)confirms the transport of photo-generated electrons from CN to SCN.Density functional theory(DFT)calculations reveal that the generation of a built-in electric field between SCN and CN enables the carrier separation to be more efficient and effective.Femtosecond transient absorption spectrum(fs-TAS)indicates prolonged lifetimes of SCN-tm/CN_(3)(τ1:9.7,τ2:110,andτ3:1343.5 ps)in comparison to those of CN(τ1:4.86,τ2:55.2,andτ3:927 ps),signifying that the construction of homojunction promotes the separation and transport of electron hole pairs,thus favoring the photocatalytic process.Under visible light irradiation,the optimized SCN-tm/CN_(3)exhibits excellent photocatalytic activity with the hydrogen evolution rate of 5407.3μmol·g^(−1)·h^(−1),which is 20.4 times higher than that of CN(265.7μmol·g^(−1)·h^(−1)).Moreover,the homojunction also displays an apparent quantum efficiency of 26.8%at 435 nm as well as ultra-long and ultra-stable cycle ability.This work offers a new strategy to construct highly efficient photocatalysts based on the metal-free conjugated polymeric CN for realizing solar energy conversion.展开更多
基金financial supports from National Key Research and Development Program of China (2021YFB2801900,2021YFB2801901,2021YFB2801902,2021YFB2801904)National Natural Science Foundation of China (No.61974177)+1 种基金National Outstanding Youth Science Fund Project of National Natural Science Foundation of China (62022062)The Fundamental Research Funds for the Central Universities (QTZX23041).
文摘Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture.Linear weighting and nonlinear spike activation are two fundamental functions of a photonic spiking neural network(PSNN).However,they are separately implemented with different photonic materials and devices,hindering the large-scale integration of PSNN.Here,we propose,fabricate and experimentally demonstrate a photonic neuro-synaptic chip enabling the simultaneous implementation of linear weighting and nonlinear spike activation based on a distributed feedback(DFB)laser with a saturable absorber(DFB-SA).A prototypical system is experimentally constructed to demonstrate the parallel weighted function and nonlinear spike activation.Furthermore,a fourchannel DFB-SA laser array is fabricated for realizing matrix convolution of a spiking convolutional neural network,achieving a recognition accuracy of 87%for the MNIST dataset.The fabricated neuro-synaptic chip offers a fundamental building block to construct the large-scale integrated PSNN chip.
基金supports from the National Key Research and Development Program of China (Nos.2021YFB2801900,2021YFB2801901,2021YFB2801902,2021YFB2801903,2021YFB2801904)the National Outstanding Youth Science Fund Project of National Natural Science Foundation of China (No.62022062)+1 种基金the National Natural Science Foundation of China (No.61974177)the Fundamental Research Funds for the Central Universities (No.QTZX23041).
文摘Spiking neural networks(SNNs)utilize brain-like spatiotemporal spike encoding for simulating brain functions.Photonic SNN offers an ultrahigh speed and power efficiency platform for implementing high-performance neuromorphic computing.Here,we proposed a multi-synaptic photonic SNN,combining the modified remote supervised learning with delayweight co-training to achieve pattern classification.The impact of multi-synaptic connections and the robustness of the network were investigated through numerical simulations.In addition,the collaborative computing of algorithm and hardware was demonstrated based on a fabricated integrated distributed feedback laser with a saturable absorber(DFB-SA),where 10 different noisy digital patterns were successfully classified.A functional photonic SNN that far exceeds the scale limit of hardware integration was achieved based on time-division multiplexing,demonstrating the capability of hardware-algorithm co-computation.
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.
基金National Key Research and Development Program of China(2021YFB2801900,2021YFB2801902,2021YFB2801904,2018YFE0201200)National Outstanding Youth Science Fund of National Natural Science Foundation of China(62022062)+1 种基金National Natural Science Foundation of China(61974177)Fundamental Research Funds for the Central Universities(QTZX23041)。
文摘We proposed and experimentally demonstrated a simple and novel photonic spiking neuron based on a distributed feedback(DFB)laser chip with an intracavity saturable absorber(SA).The DFB laser with an intracavity SA(DFBSA)contains a gain region and an SA region.The gain region is designed and fabricated by the asymmetric equivalentπ-phase shift based on the reconstruction-equivalent-chirp technique.Under properly injected current in the gain region and reversely biased voltage in the SA region,periodic self-pulsation was experimentally observed due to the Q-switching effect.The self-pulsation frequency increases with the increase of the bias current and is within the range of several gigahertz.When the bias current is below the self-pulsation threshold,neuronlike spiking responses appear when external optical stimulus pulses are injected.Experimental results show that the spike threshold,temporal integration,and refractory period can all be observed in the fabricated DFB-SA chip.To numerically verify the experimental findings,a time-dependent coupled-wave equation model was developed,which described the physics processes inside the gain and SA regions.The numerical results agree well with the experimental measurements.We further experimentally demonstrated that the weighted sum output can readily be encoded into the self-pulsation frequency of the DFB-SA neuron.We also benchmarked the handwritten digit classification task with a simple single-layer fully connected neural network.By using the experimentally measured dependence of the self-pulsation frequency on the bias current in the gain region as an activation function,we can achieve a recognition accuracy of 92.2%,which bridges the gap between the continuous valued artificial neural networks and spike-based neuromorphic networks.To the best of our knowledge,this is the first experimental demonstration of a photonic integrated spiking neuron based on a DFB-SA,which shows great potential to realizing large-scale multiwavelength photonic spiking neural network chips.
基金National Key Research and Development Program of China(2021YFB2801900,2021YFB2801902,2021YFB2801904)National Natural Science Foundation of China(61974177,61674119,62204196,62205258)+1 种基金National Outstanding Youth Science Fund Project of National Natural Science Foundation of China(62022062)Fundamental Research Funds for the Central Universities(QTZX23041,XJS220124)。
文摘Dendrites,branches of neurons that transmit signals between synapses and soma,play a vital role in spiking information processing,such as nonlinear integration of excitatory and inhibitory stimuli.However,the investigation of nonlinear integration of dendrites in photonic neurons and the fabrication of photonic neurons including dendritic nonlinear integration in photonic spiking neural networks(SNNs)remain open problems.Here,we fabricate and integrate two dendrites and one soma in a single Fabry–Perot laser with an embedded saturable absorber(FP-SA)neuron to achieve nonlinear integration of excitatory and inhibitory stimuli.Note that the two intrinsic electrodes of the gain section and saturable absorber(SA)section in the FP-SA neuron are defined as two dendrites for two ports of stimuli reception,with one electronic dendrite receiving excitatory stimulus and the other receiving inhibitory stimulus.The stimuli received by two electronic dendrites are integrated non-linearly in a single FP-SA neuron,which generates spikes for photonic SNNs.The properties of frequency encoding and spatiotemporal encoding are investigated experimentally in a single FP-SA neuron with two electronic dendrites.For SNNs equipped with FP-SA neurons,the range of weights between presynaptic neurons and postsynaptic neurons is varied from negative to positive values by biasing the gain and SA sections of FP-SA neurons.Compared with SNN with all-positive weights realized by only biasing the gain section of photonic neurons,the recognition accuracy of Iris flower data is improved numerically in SNN consisting of FP-SA neurons.The results show great potential for multi-functional integrated photonic SNN chips.
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.
基金the Natural Science Foundation of Henan(No.232300421361)the National Natural Science Foundation of China(Nos.21671176 and 21001096).
文摘S-scheme possesses superior redox capabilities compared with the II-scheme,providing an effective method to solve the innate defects of g-C_(3)N_(4)(CN).In this study,S-doped g-C_(3)N_(4)/g-C_(3)N_(4)(SCN-tm/CN)S-scheme homojunction was constructed by rationally integrating morphology control with interfacial engineering to enhance the photocatalytic hydrogen evolution performance.In-situ Kelvin probe force microscopy(KPFM)confirms the transport of photo-generated electrons from CN to SCN.Density functional theory(DFT)calculations reveal that the generation of a built-in electric field between SCN and CN enables the carrier separation to be more efficient and effective.Femtosecond transient absorption spectrum(fs-TAS)indicates prolonged lifetimes of SCN-tm/CN_(3)(τ1:9.7,τ2:110,andτ3:1343.5 ps)in comparison to those of CN(τ1:4.86,τ2:55.2,andτ3:927 ps),signifying that the construction of homojunction promotes the separation and transport of electron hole pairs,thus favoring the photocatalytic process.Under visible light irradiation,the optimized SCN-tm/CN_(3)exhibits excellent photocatalytic activity with the hydrogen evolution rate of 5407.3μmol·g^(−1)·h^(−1),which is 20.4 times higher than that of CN(265.7μmol·g^(−1)·h^(−1)).Moreover,the homojunction also displays an apparent quantum efficiency of 26.8%at 435 nm as well as ultra-long and ultra-stable cycle ability.This work offers a new strategy to construct highly efficient photocatalysts based on the metal-free conjugated polymeric CN for realizing solar energy conversion.
