arXiv subjects

Yue Hao

Publications and source records attributed to Yue Hao.

At least 37 records · Page 2Linked to original sources

arXiv2026

A Grouped Sorting Queue Supporting Dynamic Updates for Timer Management in High-Speed Network Interface Cards

With the hardware offloading of network functions, network interface cards (NICs) undertake massive stateful, high-precision, and high-throughput tasks, where timers serve as a critical enabling component. However, existing timer management schemes suffer from heavy software load, low precision, lack of hardware update support, and overflow. This paper proposes two novel operations for priority queues--update and group sorting--to enable hardware timer management. To the best of our knowledge, this work presents the first hardware priority queue to support an update operation through the composition and propagation of basic operations to modify the priorities of elements within the queue. The group sorting mechanism ensures correct timing behavior post-overflow by establishing a group boundary priority to alter the sorting process and element insertion positions. Implemented with a hybrid architecture of a one-dimension (1D) systolic array and shift registers, our design is validated through packet-level simulations for flow table timeout management. Results demonstrate that a 4K-depth, 16-bit timer queue achieves over 500 MHz (175 Mpps, 12 ns precision) in a 28nm process and over 300 MHz (116 Mpps) on an FPGA. Critically, it reduces LUTs and FFs usage by 31% and 25%, respectively, compared to existing designs.

cs.DS↗

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

Frequency Extraction from Invariant Flows

In non-degenerate integrable Hamiltonian systems, invariant tori can be parameterized equivalently by action variables or by their fundamental frequencies. We introduce an invariant-flow formulation for extracting fundamental frequencies of integrable Hamiltonian systems. By treating invariants as generators of commuting Hamiltonian flows, the frequencies are obtained from time-of-flight parameters along these flows, providing a direct alternative to action-angle constructions and spectral methods based on long time series. The approach yields an explicit numerical procedure that extends naturally to systems with multiple degrees of freedom. Its effectiveness is demonstrated using the McMillan map, where machine-precision accuracy is achieved.

nlin.SI↗

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

A multi-language auto-differentiation module and its application to a parallel particle-in-cell code on distributed computers

The auto differentiable simulation is a type of simulation that outputs of the simulation include not only the simulation result itself, but also their derivatives with respect to various input parameters. It provides an efficient method to study sensitivity of the simulation results with respect to the input parameters. Furthermore, it can be used in gradient based optimization methods for rapidly optimizing design parameters. In this paper, we present the development of a fast and transparent auto-differentiation module/class designed for easy integration into numerous simulation codes. As an application, this auto-differentiation module is integrated into a parallel particle-in-cell code with message passing interface (MPI) on distributed memory computers.

physics.comp-ph↗

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

Design of a Timer Queue Supporting Dynamic Update Operations

Large-scale timers are ubiquitous in network processing, including flow table entry expiration control in software defined network (SDN) switches, MAC address aging in Ethernet bridges, and retransmission timeout management in TCP/IP protocols. Conventional implementations suffer from critical limitations: low timing accuracy due to large-scale timer traversal and high computational overhead for new timer insertion. This paper presents a hybrid-architecture hardware priority queue based on systolic arrays and shift registers for efficient timer queue management. The design uniquely supports five operations: enqueue, dequeue, delete, update, and peek.To the best of our knowledge, it is the first hardware priority queue enabling in-queue priority updates. By leveraging centralized Boolean logic encoding within systolic blocks, the design efficiently generates set/shift control signals while the novel push-first operation ensures FIFO ordering for same-priority timers without additional metadata. Experimental results demonstrate that the design operates at over 400 MHz on FPGAs, achieving a 2.2-2.8x reduction in resource consumption compared to state-of-the-art implementations.

cs.NI↗

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↗

arXiv2025

Construction of approximate invariants for non-integrable Hamiltonian systems

We present a method to construct high-order polynomial approximate invariants (AI) for non-integrable Hamiltonian dynamical systems, and apply it to modern ring-based particle accelerators. Taking advantage of a special property of one-turn transformation maps in the form of a square matrix, AIs can be constructed order-by-order iteratively. Evaluating AI with simulation data, we observe that AI's fluctuation is actually a measure of chaos. Through minimizing the fluctuations with control knobs in accelerators, the stable region of long-term motions could be enlarged.

nlin.CD↗

arXiv2025

Wurtzite AlScN/AlN Superlattice Ferroelectrics Enable Endurance Beyond 1010 Cycles

Wurtzite ferroelectrics are rapidly emerging as a promising material class for next-generation non-volatile memory technologies, owing to their large remanent polarization, intrinsically ordered three-dimensional crystal structure, and full compatibility with CMOS processes and back-end-of-line (BEOL) integration. However, their practical implementation remains critically constrained by a severe endurance bottleneck: under conditions where the remanent polarization (2Pr) reaches or exceeds 200 uC/cm^2, devices typically undergo catastrophic failure before reaching 10^8 cycles. Here, we report a vacancy-confining superlattice strategy that addresses this limitation, achieving reliable ferroelectric switching beyond 10^10 cycles while preserving saturated polarization (2Pr >= 200 uC/cm^2). This is achieved by embedding periodic ultrathin AlN layers within AlScN films, forming wurtzite AlScN/AlN superlattices, in conjunction with a dynamic recovery protocol that actively stabilizes the defect landscape throughout repeated cycling. Atomic-resolution imaging and EELS spectrum imaging technique, supported by first-principles calculations, reveal a self-regulated defect topology in which nitrogen vacancies are spatially confined by heterostructure energy barriers and dynamically re-trapped into energetically favorable lattice sites. This dual spatial-energetic confinement mechanism effectively inhibits both long-range percolative migration and local defect clustering, enabling such an ultrahigh endurance exceeding 10^10 cycles and limiting polarization degradation to below 3% after 10^9 cycles. These findings establish nitrogen vacancy topology stabilization as a foundational design principle for reliable operation of wurtzite ferroelectrics, providing a scalable and CMOS-compatible platform for future high-endurance ferroelectric memory technologies.

cond-mat.mtrl-sci↗

arXiv2025

Dynamic Focusing to Suppress Emittance Transfer in Crab-Crossing Flat Beam Collisions

Flat hadron beam collisions, though expected to enhance peak luminosity by about an order of magnitude, have not yet been demonstrated. Our study reveals a critical limitation: realistic fluctuations, when amplified by synchro-betatron resonance, lead to transverse emittance transfer in flat-beam collisions. Using beam-beam simulations based on Electron-Ion Collider design parameters, we show that this effect leads to vertical emittance growth, which can distort the flat-beam profile and degrade luminosity. We propose a dynamic focusing scheme that combines sextupoles with crab cavities to suppress the hourglass-induced resonance. This approach increases tolerance to fluctuations and improves the robustness of flat-beam collisions. This practical mitigation facilitates the adoption of flat-beam collisions in next-generation lepton-hadron colliders.

physics.acc-ph↗

arXiv2025

Cryogenic Ferroelectric Behavior of Wurtzite Ferroelectrics

This study presents the first experimental exploration into cryogenic ferroelectric behavior in wurtzite ferroelectrics. A breakdown field (EBD) to coercive field (EC) ratio of 1.8 is achieved even at 4 K, marking the lowest ferroelectric switching temperature reported for wurtzite ferroelectrics. Additionally, a significant evolution in fatigue behavior is captured, transitioning from hard breakdown to ferroelectricity loss at cryogenic temperatures. These findings unlock the feasibility for wurtzite ferroelectrics to advance wide temperature non-volatile memory.

cond-mat.mtrl-sci↗

arXiv2025

Efficient second-harmonic emission via strong modal overlap in single-resonant lithium niobate nanocavity

High-efficiency second-harmonic generation (SHG) in compact integrated photonic systems is crucial for advancing nonlinear optical technologies. However, achieving exceptional conversion efficiencies while maintaining stable performance remains a significant challenge. Here, we report a high-Q single-resonant photonic crystal nanobeam cavity (PCNBC) on a polymer-loaded lithium niobate on insulator (LNOI) platform, which enables bright second-harmonic (SH) emission. Through synergistic optimization of modal confinement and spatial overlap in a y-cut LN architecture, our device achieves a normalized SHG conversion efficiency of 163%/W, outperforming previous LN-based photonic crystal cavities LN-based photonic crystal cavities by over three orders of magnitude. The visible SH emission at 768.77 nm exhibits a single-lobe radiation pattern with precise spectral alignment between fundamental (FH) and second-harmonic (SH) modes, a critical feature for integrated photonic circuits. Remarkably, the conversion efficiency remains stable under thermal variations up to 20°C, addressing a key limitation of multi-resonant systems. High-order cavity modes are directly visualized via CCD imaging, confirming strong spatial overlap. This work establishes a record SHG conversion efficiency for LN microcavities and provides a scalable, temperature-insensitive architecture for nonlinear light sources, with immediate applications in quantum optics and chip-scale interconnects.

physics.optics↗

arXiv2025

DQO-MAP: Dual Quadrics Multi-Object mapping with Gaussian Splatting

Accurate object perception is essential for robotic applications such as object navigation. In this paper, we propose DQO-MAP, a novel object-SLAM system that seamlessly integrates object pose estimation and reconstruction. We employ 3D Gaussian Splatting for high-fidelity object reconstruction and leverage quadrics for precise object pose estimation. Both of them management is handled on the CPU, while optimization is performed on the GPU, significantly improving system efficiency. By associating objects with unique IDs, our system enables rapid object extraction from the scene. Extensive experimental results on object reconstruction and pose estimation demonstrate that DQO-MAP achieves outstanding performance in terms of precision, reconstruction quality, and computational efficiency. The code and dataset are available at: https://github.com/LiHaoy-ux/DQO-MAP.

cs.CV↗

arXiv2024

JuTrack: a Julia package for auto-differentiable accelerator modeling and particle tracking

Efficient accelerator modeling and particle tracking are key for the design and configuration of modern particle accelerators. In this work, we present JuTrack, a nested accelerator modeling package developed in the Julia programming language and enhanced with compiler-level automatic differentiation (AD). With the aid of AD, JuTrack enables rapid derivative calculations in accelerator modeling, facilitating sensitivity analyses and optimization tasks. We demonstrate the effectiveness of AD-derived derivatives through several practical applications, including sensitivity analysis of space-charge-induced emittance growth, nonlinear beam dynamics analysis for a synchrotron light source, and lattice parameter tuning of the future Electron-Ion Collider (EIC). Through the incorporation of automatic differentiation, this package opens up new possibilities for accelerator physicists in beam physics studies and accelerator design optimization.

physics.acc-ph↗

arXiv2024

Business Analysis: User Attitude Evaluation and Prediction Based on Hotel User Reviews and Text Mining

In the post-pandemic era, the hotel industry plays a crucial role in economic recovery, with consumer sentiment increasingly influencing market trends. This study utilizes advanced natural language processing (NLP) and the BERT model to analyze user reviews, extracting insights into customer satisfaction and guiding service improvements. By transforming reviews into feature vectors, the BERT model accurately classifies emotions, uncovering patterns of satisfaction and dissatisfaction. This approach provides valuable data for hotel management, helping them refine service offerings and improve customer experiences. From a financial perspective, understanding sentiment is vital for predicting market performance, as shifts in consumer sentiment often correlate with stock prices and overall industry performance. Additionally, the study addresses data imbalance in sentiment analysis, employing techniques like oversampling and undersampling to enhance model robustness. The results offer actionable insights not only for the hotel industry but also for financial analysts, aiding in market forecasts and investment decisions. This research highlights the potential of sentiment analysis to drive business growth, improve financial outcomes, and enhance competitive advantage in the dynamic tourism and hospitality sectors, thereby contributing to the broader economic landscape.

cs.CY↗

arXiv2024

Cavity-enhanced acousto-optic modulators on polymer-loaded lithium niobate integrated platform

On chip acousto-optic (AO) modulation represents a significant advancement in the development of highly integrated information processing systems. However, conventional photonic devices face substantial challenges in achieving efficient conversion due to the limited overlap between acoustic waves and optical waves. In this study, we address this limitation by demonstrating an enhanced conversion effect of photonic crystal nanobeam cavities (PCNBCs) in AO modulation on a polymer-loaded lithium niobate integrated platform. Attributed to the high ratio of quality factor (Q) to mode volume (V) and optimal light-sound overlap within the nanocavity, PCNBCs-based AO modulator exhibits a significantly enhanced extinction ratio of 38 dB with a threshold RF power below -50 dBm, which is two orders of magnitude lower than that based on micro-ring resonator (MRRs). In addition, robust digital amplitude shift keying modulations using selected RF and optical channels of the PCNBCs-enhanced AO modulators. These findings validate the compelling properties of the PCNBCs photonic platform, establishing it as a promising candidate for on-chip integrated microwave photonics, optical transceivers, and computing applications.

physics.optics↗