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
As Moore’s law has reached its limits,it is becoming increasingly difficult for traditional computing architectures to meet the demands of continued growth in computing power.Photonic neural computing has become a pr...As Moore’s law has reached its limits,it is becoming increasingly difficult for traditional computing architectures to meet the demands of continued growth in computing power.Photonic neural computing has become a promising approach to overcome the von Neuman bottleneck.However,while photonic neural networks are good at linear computing,it is difficult to achieve nonlinear computing.Here,we propose and experimentally demonstrate a coherent photonic spiking neural network consisting of Mach–Zehnder modulators(MZMs)as the synapse and an integrated quantum-well Fabry–Perot laser with a saturable absorber(FP-SA)as the photonic spiking neuron.Both linear computation and nonlinear computation are realized in the experiment.In such a coherent architecture,two presynaptic signals are modulated and weighted with two intensity modulation MZMs through the same optical carrier.The nonlinear neuron-like dynamics including temporal integration,threshold,and refractory period are successfully demonstrated.Besides,the effects of frequency detuning on the nonlinear neuron-like dynamics are also explored,and the frequency detuning condition is revealed.The proposed hardware architecture plays a foundational role in constructing a large-scale coherent photonic spiking neural network.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 chi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National Key Research and Development Program of China(2021YFB2801900,2021YFB2801901,2021YFB2801902,2021YFB2801904)National Natural Science Foundation of China(61974177,61674119)+1 种基金Outstanding Youth Science Fund of National Natural Science Foundation of China(62022062)Fundamental Research Funds for the Central Universities(JB210114)。
文摘As Moore’s law has reached its limits,it is becoming increasingly difficult for traditional computing architectures to meet the demands of continued growth in computing power.Photonic neural computing has become a promising approach to overcome the von Neuman bottleneck.However,while photonic neural networks are good at linear computing,it is difficult to achieve nonlinear computing.Here,we propose and experimentally demonstrate a coherent photonic spiking neural network consisting of Mach–Zehnder modulators(MZMs)as the synapse and an integrated quantum-well Fabry–Perot laser with a saturable absorber(FP-SA)as the photonic spiking neuron.Both linear computation and nonlinear computation are realized in the experiment.In such a coherent architecture,two presynaptic signals are modulated and weighted with two intensity modulation MZMs through the same optical carrier.The nonlinear neuron-like dynamics including temporal integration,threshold,and refractory period are successfully demonstrated.Besides,the effects of frequency detuning on the nonlinear neuron-like dynamics are also explored,and the frequency detuning condition is revealed.The proposed hardware architecture plays a foundational role in constructing a large-scale coherent photonic spiking neural network.
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 chi
