arXiv subjects

Xingxing Guo

Publications and source records attributed to Xingxing Guo.

11 recordsLinked to original sources

arXiv2026

Hardware Implementation of Photonic Spiking Hash Retrieval

Hashing retrieval is a pivotal technology for large-scale similarity search, widely applied in retrieval-augmented generation (RAG) for large language models (LLMs), massive image repositories, and bioinformatics sequence matching. However, traditional electronic hashing implementations face severe bottlenecks in power consumption and latency when processing high-dimensional data, while existing photonic neural networks often lack robust mechanisms for direct binary code generation under analog noise. To address these challenges, we propose a hardware-software co-designed photonic spiking hashing framework. We utilize the nonlinear thresholding dynamics of a distributed feedback laser with saturable absorber (DFB-SA) to realize the final binarization of a single-step spiking neural network (SNN). Crucially, a hardware-aware quantization margin loss is introduced to maximize the decision margin, effectively mitigating bit flips caused by optical intensity fluctuations. Validated on MNIST (image) and 20 Newsgroups (text) datasets, our system demonstrates robust binary code generation and high retrieval accuracy comparable to digital baselines. Most significantly, the proposed photonic architecture exhibits superior efficiency with an encoding latency of 2.294 ns/query and an energy consumption of 73.70 pJ/query. This work offers a robust and viable path for ultra-fast, energy-efficient optoelectronic neuromorphic computing in high-throughput information retrieval tasks.

physics.optics↗

arXiv2026

Hardware implementation of photonic neuromorphic autonomous navigation

Reinforcement learning (RL) is a core technology enabling the transition of artificial intelligence (AI) from perception to decision-making, but its deployment on conventional electronic hardware suffers from high latency and energy consumption imposed by the von Neumann architecture. Here, we propose a photonic spiking twin delayed deep deterministic policy gradient (TD3) reinforcement learning architecture for neuromorphic autonomous navigation and experimentally validate it on a distributed feedback laser with a saturable absorber (DFB-SA) array. The hybrid architecture integrates a photonic spiking Actor network with dual continuous-valued Critic networks, where the final nonlinear spiking activation layer of the Actor is deployed on the DFB-SA laser array. In autonomous navigation tasks, the system achieves an average reward of 58.22 plus-minus 17.29 and a success rate of 80% plus-minus 8.3%. Hardware-software co-inference demonstrates an estimated energy consumption of 0.78 nJ/inf and an ultra-low latency of 191.20 ps/inf, with co-inference error rates of 0.051% and 0.059% in task scenarios with and without obstacle interference, respectively. Simulations for error-activated channels show full agreement with the expected responses, validating the dynamic characteristics of the DFB-SA laser. The architecture shows strong potential for integration with large-scale photonic linear computing chips, enabling fully-functional photonic computation and low-power, low-latency neuromorphic autonomous navigation.

physics.optics↗

arXiv2026

Photonic spiking reinforcement learning for intelligent routing

Intelligent routing plays a key role in modern communication infrastructure, including data centers, computing networks, and future 6G networks. Although reinforcement learning (RL) has shown great potential for intelligent routing, its practical deployment remains constrained by high energy consumption and decision latency. Here, we propose a photonic spiking RL architecture that implements a proximal policy optimization (PPO)-based intelligent routing algorithm. The performance of the proposed approach is systematically evaluated on a software-defined network (SDN) with a fat-tree topology. The results demonstrate that, under various baseline traffic rate conditions, the PPO-based routing strategy significantly outperforms the conventional Dijkstra algorithm in several key performance metrics. Furthermore, a hardware-software collaborative framework of the spiking Actor network is realized for three typical baseline traffic rates, utilizing a photonic synapse chip based on a Mach-Zehnder interferometer (MZI) array and a photonic spiking neuron chip based on distributed feedback lasers with a saturable absorber (DFB-SAs). Experimental validation on 640 state-action pairs shows that the inference accuracy of the hardware-software collaborative framework is consistent with that of the pure algorithmic implementation. The impacts of different hidden-layer scales in the spiking Actor network and varying network size of fat-tree topology are further analyzed. The integration of photonic spiking RL with SDN-based routing establishes a novel paradigm for intelligent routing optimization, featuring ultra-low latency and high energy efficiency. This approach exhibits broad application prospects in real-time network optimization scenarios, including large-scale data centers, computing networks, satellite Internet systems, and future 6G networks.

physics.optics↗

arXiv2025

Photonic Spiking Graph Neural Network for Energy-Efficient Structured Data Processing

Photonic computing shows great potential for signal processing and artificial intelligence (AI) acceleration due to its ultra-high speed, low energy consumption, and inherent parallelism. Existing photonic computing research has mainly focused on convolutional neural networks (CNNs) and fully connected neural networks (FCNNs), which are well suited for tasks such as image classification and object detection but face limitations in handling graph-structured data. Graph neural networks (GNNs) are specifically designed to model complex relational structures. In this work, we propose a photonic spiking graph neural network (PSGNN) architecture that integrates the structural modeling capability of GNNs, the temporal dynamics of spiking neurons, and the parallel computing advantages of photonic hardware. Through hardware-software co-optimization, a bias-term simulation method tailored for photonic chips is implemented using feature-dimension expansion, enabling effective network training. Experiments on the KarateClub and PubMed datasets achieve training accuracies of 100 percent (92 +/- 2 percent) and test accuracies of 97 percent (90 +/- 1 percent). A silicon photonics 4 x 4 Mach-Zehnder interferometer (MZI) array is further constructed for hardware validation, achieving a test accuracy of 93 percent. The system demonstrates an inference latency of 97 ps, with an energy efficiency of 280 GOPS/W and a computational density of 52 GOPS/mm^2. These results highlight the potential of PSGNN for structured-data processing applications.

physics.optics↗

arXiv2025

Hardware-aware Lightweight Photonic Spiking Neural Network for Pattern Classification

There exists a significant scale gap between photonic neural network integrated chips and neural networks, which hinders the deployment and application of photonic neural network. Here, we propose hardware-aware lightweight spiking neural networks (SNNs) architecture tailored to our photonic neuromorphic chips, and conducts hardware-software collaborative computing for solving patter classification tasks. Here, we employed a simplified Mach-Zehnder interferometer (MZI) mesh for performing linear computation, and 16-channel distributed feedback lasers with saturable absorber (DFB-SA) array for performing nonlinear spike activation. Both photonic neuromorphic chips based on the MZI mesh and DFB-SA array were designed, optimized and fabricated. Furthermore, we propose a lightweight spiking neural network (SNN) with discrete cosine transform to reduce input dimension and match the input/output ports number of the photonic neuromorphic chips. We demonstrated an end-to-end inference of an entire layer of the lightweight photonic SNN. The hardware-software collaborative inference accuracy is 90% and 80.5% for MNIST and Fashion-MNIST datasets, respectively. The energy efficiency is 1.39 TOPS/W for the MZI mesh, and is 987.65 GOPS/W for the DFB-SA array. The lightweight architecture and experimental demonstration address the challenge of scale mismatch between the photonic chip and SNN, paving the way for the hardware deployment of photonic SNNs.

physics.optics↗

arXiv2025

Hardware-Software Collaborative Computing of Photonic Spiking Reinforcement Learning for Robotic Continuous Control

Robotic continuous control tasks impose stringent demands on the energy efficiency and latency of computing architectures due to their high-dimensional state spaces and real-time interaction requirements. Conventional electronic computing platforms face computational bottlenecks, whereas the fusion of photonic computing and spiking reinforcement learning (RL) offers a promising alternative. Here, we propose a novel computing architecture based on photonic spiking RL, which integrates the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm with spiking neural network (SNN). The proposed architecture employs an optical-electronic hybrid computing paradigm wherein a silicon photonic Mach-Zehnder interferometer (MZI) chip executes linear matrix computations, while nonlinear spiking activations are performed in the electronic domain. Experimental validation on the Pendulum-v1 and HalfCheetah-v2 benchmarks demonstrates the system capability for software-hardware co-inference, achieving a control policy reward of 5831 on HalfCheetah-v2, a 23.33% reduction in convergence steps, and an action deviation below 2.2%. Notably, this work represents the first application of a programmable MZI photonic computing chip to robotic continuous control tasks, attaining an energy efficiency of 1.39 TOPS/W and an ultralow computational latency of 120 ps. Such performance underscores the promise of photonic spiking RL for real-time decision-making in autonomous and industrial robotic systems.

cs.RO↗

arXiv2025

Integrated photonic neuromorphic computing: device, architecture, chip, algorithm

Artificial intelligence (AI) has experienced explosive growth in recent years. The large models have been widely applied in various fields, including natural language processing, image generation, and complex decision-making systems, revolutionizing technological paradigms across multiple industries. Nevertheless, the substantial data processing demands during model training and inference result in the computing power bottleneck. Traditional electronic chips based on the von Neumann architecture struggle to meet the growing demands for computing power and power efficiency amid the continuous development of AI. Photonic neuromorphic computing, an emerging solution in the post-Moore era, exhibits significant development potential. Leveraging the high-speed and large-bandwidth characteristics of photons in signal transmission, as well as the low-power consumption advantages of optical devices, photonic integrated computing chips have the potential to overcome the memory wall and power wall issues of electronic chips. In recent years, remarkable advancements have been made in photonic neuromorphic computing. This article presents a systematic review of the latest research achievements. It focuses on fundamental principles and novel neuromorphic photonic devices, such as photonic neurons and photonic synapses. Additionally, it comprehensively summarizes the network architectures and photonic integrated neuromorphic chips, as well as the optimization algorithms of photonic neural networks. In addition, combining with the current status and challenges of this field, this article conducts an in-depth discussion on the future development trends of photonic neuromorphic computing in the directions of device integration, algorithm collaborative optimization, and application scenario expansion, providing a reference for subsequent research in the field of photonic neuromorphic computing.

physics.optics↗

arXiv2025

Nonlinear Photonic Neuromorphic Chips for Spiking Reinforcement Learning

Photonic computing chips have made significant progress in accelerating linear computations, but nonlinear computations are usually implemented in the digital domain, which introduces additional system latency and power consumption, and hinders the implementation of fully-functional photonic neural network chips. Here, we propose and fabricate a 16-channel programmable incoherent photonic neuromorphic computing chip by co-designing a simplified MZI mesh and distributed feedback lasers with saturable absorber array using different materials, enabling implementation of both linear and nonlinear spike computations in the optical domain. Furthermore, previous studies mainly focused on supervised learning and simple image classification tasks. Here, we propose a photonic spiking reinforcement learning (RL) architecture for the first time, and develop a software-hardware collaborative training-inference framework to address the challenge of training spiking RL models. We achieve large-scale, energy-efficient (photonic linear computation: 1.39 TOPS/W, photonic nonlinear computation: 987.65 GOPS/W) and low-latency (320 ps) end-to-end deployment of an entire layer of photonic spiking RL. Two RL benchmarks include the discrete CartPole task and the continuous Pendulum tasks are demonstrated experimentally based on spiking proximal policy optimization algorithm. The hardware-software collaborative computing reward value converges to 200 (-250) for the CartPole tasks, respectively, comparable to that of a traditional PPO algorithm. This experimental demonstration addresses the challenge of the absence of large-scale photonic nonlinear spike computation and spiking RL training difficulty, and presents a high-speed and low-latency photonic spiking RL solution with promising application prospects in fields such as real-time decision-making and control for robots and autonomous driving.

physics.optics↗

arXiv2023

Photonic Integrated Neuro-Synaptic Core for Convolutional Spiking Neural Network

Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture. Linear weighting and nonlinear spiking 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 spiking 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 four-channel DFB-SA 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.

physics.optics↗

arXiv2022

Spiking SiamFC++: Deep Spiking Neural Network for Object Tracking

Spiking neural network (SNN) is a biologically-plausible model and exhibits advantages of high computational capability and low power consumption. While the training of deep SNN is still an open problem, which limits the real-world applications of deep SNN. Here we propose a deep SNN architecture named Spiking SiamFC++ for object tracking with end-to-end direct training. Specifically, the AlexNet network is extended in the time domain to extract the feature, and the surrogate gradient function is adopted to realize direct supervised training of the deep SNN. To examine the performance of the Spiking SiamFC++, several tracking benchmarks including OTB2013, OTB2015, VOT2015, VOT2016, and UAV123 are considered. It is found that, the precision loss is small compared with the original SiamFC++. Compared with the existing SNN-based target tracker, e.g., the SiamSNN, the precision (succession) of the proposed Spiking SiamFC++ reaches 85.24% (64.37%), which is much higher than that of 52.78% (44.32%) achieved by the SiamSNN. To our best knowledge, the performance of the Spiking SiamFC++ outperforms the existing state-of-the-art approaches in SNN-based object tracking, which provides a novel path for SNN application in the field of target tracking. This work may further promote the development of SNN algorithms and neuromorphic chips.

cs.CV↗

arXiv2022

Hardware-algorithm collaborative computing with photonic spiking neuron chip based on integrated Fabry-Pérot laser with saturable absorber

Photonic neuromorphic computing has emerged as a promising avenue toward building a low-latency and energy-efficient non-von-Neuman computing system. Photonic spiking neural network (PSNN) exploits brain-like spatiotemporal processing to realize high-performance neuromorphic computing. However, the nonlinear computation of PSNN remains a significant challenging. Here, we proposed and fabricated a photonic spiking neuron chip based on an integrated Fabry-Pérot laser with a saturable absorber (FP-SA) for the first time. The nonlinear neuron-like dynamics including temporal integration, threshold and spike generation, refractory period, and cascadability were experimentally demonstrated, which offers an indispensable fundamental building block to construct the PSNN hardware. Furthermore, we proposed time-multiplexed spike encoding to realize functional PSNN far beyond the hardware integration scale limit. PSNNs with single/cascaded photonic spiking neurons were experimentally demonstrated to realize hardware-algorithm collaborative computing, showing capability in performing classification tasks with supervised learning algorithm, which paves the way for multi-layer PSNN for solving complex tasks.

cs.ET↗