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Efficient rock joint detection from large-scale 3D point clouds using vectorization and parallel computing approaches 认领 引用
1
作者 Yunfeng Ge Zihao Li +2 位作者 Huiming Tang Qian Chen Zhongxu Wen 《Geoscience Frontiers》 SCIE CAS CSCD 2025年第5期1-15,共15页
The application of three-dimensional(3D)point cloud parametric analyses on exposed rock surfaces,enabled by Light Detection and Ranging(LiDAR)technology,has gained significant popularity due to its efficiency and the ... The application of three-dimensional(3D)point cloud parametric analyses on exposed rock surfaces,enabled by Light Detection and Ranging(LiDAR)technology,has gained significant popularity due to its efficiency and the high quality of data it provides.However,as research extends to address more regional and complex geological challenges,the demand for algorithms that are both robust and highly efficient in processing large datasets continues to grow.This study proposes an advanced rock joint identification algorithm leveraging artificial neural networks(ANNs),incorporating parallel computing and vectorization of high-performance computing.The algorithm utilizes point cloud attributes—specifically point normal and point curvatures-as input parameters for ANNs,which classify data into rock joints and non-rock joints.Subsequently,individual rock joints are extracted using the density-based spatial clustering of applications with noise(DBSCAN)technique.Principal component analysis(PCA)is subsequently employed to calculate their orientations.By fully utilizing the computational power of parallel computing and vectorization,the algorithm increases the running speed by 3–4 times,enabling the processing of large-scale datasets within seconds.This breakthrough maximizes computational efficiency while maintaining high accuracy(compared with manual measurement,the deviation of the automatic measurement is within 2°),making it an effective solution for large-scale rock joint detection challenges.©2025 China University of Geosciences(Beijing)and Peking University. 展开更多
关键词 Rock joints Pointclouds Artificialneuralnetwork High-performance computing Parallel computing Vectorization
Memristor devices for next-generation computing:from performance optimization to application-specific co-design 认领 引用 被引量：1
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作者 Zhaorui Liu Caifang Gao +5 位作者 Jingbo Yang Zuxin Chen Enlong Li Jun Li Mengjiao Li Jianhua Zhang 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第1期119-146,共28页
Memristors have emerged as a transformative technology in the realm of electronic devices,offering unique advantages such as fast switching speeds,low power consumption,and the ability to sensor-memory-compute.The app... Memristors have emerged as a transformative technology in the realm of electronic devices,offering unique advantages such as fast switching speeds,low power consumption,and the ability to sensor-memory-compute.The applications span across non-volatile memory,neuromorphic computing,hardware security,and beyond,prompting memristors to become a versatile solution for next-generation computing and data storage systems.Despite enormous potential of memristors,the transition from laboratory prototypes to large-scale applications is challenging in terms of material stability,device reproducibility,and array scalability.This review systematically explores recent advancements in high-performance memristor technologies,focusing on performance enhancement strategies through material engineering,structural design,pulse protocol optimization,and algorithm control.We provide an in-depth analysis of key performance metrics tailored to specific applications,including non-volatile memory,neuromorphic computing,and hardware security.Furthermore,we propose a co-design framework that integrates device-level optimizations with operational-level improvements,aiming to bridge the gap between theoretical models and practical implementations. 展开更多
关键词 memristor performance optimization device design neuromorphic computing
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Task Offloading and Edge Computing in IoT-Gaps, Challenges and Future Directions 认领 引用 被引量：1
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作者 Hitesh Mohapatra 《Computers, Materials & Continua》 SCIE EI 2026年第6期268-296,共29页
This review examines current approaches to real-time decision-making and task optimization in Internet of Things systems through the application of machine learning models deployed at the network edge.Existing literat... This review examines current approaches to real-time decision-making and task optimization in Internet of Things systems through the application of machine learning models deployed at the network edge.Existing literature shows that edge-based distributed intelligence reduces cloud dependency.It addresses transmission latency,device energy use,and bandwidth limits.Recent optimization strategies employ dynamic task offloading mechanisms to determine optimal workload placement across local devices and edge servers without centralized coordination.Empirical findings from the literature indicate performance improvements with latency reductions of approximately 32.8%and energy efficiency gains of 27.4%compared to conventional cloud-centric models.However,critical gaps remain in current methodologies.Most studies focus on static network topologies and do not adequately address load balancing across multiple edge nodes.Security vulnerabilities during task transmission are underexplored,and privacy considerations for sensitive data remain insufficiently integrated into existing frameworks.Task caching strategies and fault tolerance mechanisms require further investigation in highly dynamic environments.The ability of existing approaches to handle large-scale deployments and complex edge-cloud collaborative scenarios has not been thoroughly validated.This review synthesizes current progress while identifying fundamental challenges that must be resolved for practical deployment in time-sensitive applications spanning smart manufacturing,autonomous systems,and healthcare monitoring.Future work should prioritize robust security integration,efficient load distribution,and scalability across heterogeneous edge infrastructures. 展开更多
关键词 Edge computing task offloading deep reinforcement learning mobile edge computing IoT performance optimization methodological rigor assessment
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Computing power networks for unmanned aerial vehicles:a hierarchical resources trading market 认领 引用
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作者 Xiaofei Wang Hui Deng +3 位作者 Chao Qiu Zheyuan Chen Tao Luo Zhao Ming 《Digital Communications and Networks》 SCIE EI CSCD 2026年第4期584-593,共10页
Unmanned Aerial Vehicles(UAVs)are increasingly deployed across military and civilian domains due to their operational flexibility,low maintenance costs,and high mobility.With the growing complexity of UAV applications... Unmanned Aerial Vehicles(UAVs)are increasingly deployed across military and civilian domains due to their operational flexibility,low maintenance costs,and high mobility.With the growing complexity of UAV applications and tasks,robust support from computing power networks is essential.These networks,acting as resource integration paradigms,furnish UAVs with pooled resources to tackle extensive computational demands.In this paper,we develop a framework for trading computing power resources,modeling the transaction process through a three-stage Stackelberg game to facilitate sequential decision-making.We theoretically demonstrate the existence of a Nash equilibrium and introduce a Dynamic Game Reinforcement algorithm to identify optimal strategies.Our experimental results affirm the framework's efficacy and the superior performance of our algorithm.Additionally,we explore how variables like UAV quantity and network congestion influence the market dynamics of the computing power network. 展开更多
关键词 UAVs Cloud computing Computing power network Resource trading Stackelberg game
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Back-gate-tuned organic electrochemical transistor with temporal dynamic modulation for reservoir computing 认领 引用
5
作者 Qian Xu Jie Qiu +6 位作者 Mengyang Liu Dongzi Yang Tingpan Lan Jie Cao Yingfen Wei Hao Jiang Ming Wang 《Journal of Semiconductors》 EI CAS CSCD 2026年第1期118-123,共6页
Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal sca... Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal scale tasks.In this study,we report an OECT-based neuromorphic device with tunable relaxation time(τ)by introducing an additional vertical back-gate electrode into a planar structure.The dual-gate design enablesτreconfiguration from 93 to 541 ms.The tunable relaxation behaviors can be attributed to the combined effects of planar-gate induced electrochemical doping and back-gateinduced electrostatic coupling,as verified by electrochemical impedance spectroscopy analysis.Furthermore,we used theτ-tunable OECT devices as physical reservoirs in the RC system for intelligent driving trajectory prediction,achieving a significant improvement in prediction accuracy from below 69%to 99%.The results demonstrate that theτ-tunable OECT shows a promising candidate for multi-temporal scale neuromorphic computing applications. 展开更多
关键词 neuromorphic computing reservoir computing OECT tunable dynamics trajectory prediction
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The three-dimensional meshfree numerical manifold method based on parallel computing 认领 引用
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作者 Keqin Zhang Wei Wu +2 位作者 Danfeng Zhang Yanfei Kang Hehua Zhu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期360-371,共12页
The numerical manifold method,extensively utilized in numerical computations,faces significant challenges in generating complex manifold elements,particularly for three-dimensional applications.To overcome this challe... The numerical manifold method,extensively utilized in numerical computations,faces significant challenges in generating complex manifold elements,particularly for three-dimensional applications.To overcome this challenge,the meshfree numerical manifold method is developed by integrating the moving least-squares method into the numerical manifold method,effectively bypassing the need for meshing complex geometric objects.However,the implementation of the moving least-squares method introduces computational efficiency issues.To mitigate these,parallel computing methods have been incorporated,resulting in a tenfold increase in the speed of assembling the stiffness matrix with central processing unit parallelism,and a twentyfold increase with graphics processing unit parallelism.The static mechanical system equations for the meshfree numerical manifold method are derived using the Galerkin method.The method’s effectiveness and accuracy are then validated through a series of numerical experiments.The experiments demonstrated that the meshfree numerical manifold method achieves a high precision with minimal nodes and integration points.Additionally,positioning nodes outside the domain significantly improves computational accuracy at the boundaries. 展开更多
关键词 Meshfree numerical manifold method Three-dimensional computation Parallel computation Elastostatics Moving least-squares
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Large-scale integrated photonic accelerators for ultralow-latency and universal AI computing 认领 引用
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作者 Xiangyan Meng Junshen Li +4 位作者 Kangwei Fei Yu Wang Wei Li Nuannuan Shi Ming Li 《Journal of Semiconductors》 EI CAS CSCD 2026年第6期12-15,共4页
Integrated silicon photonics has emerged as a transformative technology for post-Moore’s law computing,offering intrinsic advantages of high bandwidth,ultralow latency and low energy consumption that far exceed tradi... Integrated silicon photonics has emerged as a transformative technology for post-Moore’s law computing,offering intrinsic advantages of high bandwidth,ultralow latency and low energy consumption that far exceed traditional electronic computing architectures[1−4].As artificial intelligence(AI)models continue to grow in complexity and scale,the demand for high-speed,energy-efficient computing has spurred intensive research into photonic computing as a promising alternative to electronic accelerators[5−7].Matrix multiply−accumulate(MAC)operations,the core of deep learning and combinatorial optimization algorithms,are particularly amenable to photonic implementation,as light enables parallel multiplication and accumulation with minimal data movement[8,9].However,the practical application of photonic computing has long been hindered by critical challenges including large-scale integration of photonic components,electro-optical co-packaging,guaranteed computation accuracy of analog photonic systems,and compatibility with mainstream AI models and algorithms[10,11]. 展开更多
关键词 photonic computing integrated silicon photonics electronic computing ultralow latency matrix multiply accumulate analog photonic systems silicon photonics electronic accelerators matrix
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Computing-centric computing-in-memory and memory-centric in-/near-memory computing for DNNs and transformer based LLMs 认领 引用
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作者 Xin Si Xing Wang Jun Yang 《Journal of Semiconductors》 EI CAS CSCD 2026年第7期7-11,共5页
Introduction.With the rapid development of transformer-based large language models(LLMs)and deep neural networks(DNNs),the demand for both high computational throughput and massive memory capacity has grown exponentia... Introduction.With the rapid development of transformer-based large language models(LLMs)and deep neural networks(DNNs),the demand for both high computational throughput and massive memory capacity has grown exponentially[1-4].In response,the 2D/3D hybrid integration of computing-centric computing-in-memory(CIM)and memory-centric in-ear-memory computing(INMC)circuits has emerged as a transformative technology.Unlike conventional von Neumann architectures,these memory-computing hybrid designs offer systematic advantages including high energy efficiency,high memory bandwidth,and sufficient on-device memory capacity[1-13]. 展开更多
关键词 von neumann architecturesthese near memory computing transformer based large language models large language models llms memory computing deep neural networks dnns memory centric computing centric
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A low-thermal-budget MOSFET-based reservoir computing for temporal data classification 认领 引用
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作者 Yanqing Li Feixiong Wang +5 位作者 Heyi Huang Yadong Zhang Xiangpeng Liang Shuang Liu Jianshi Tang Huaxiang Yin 《Journal of Semiconductors》 EI CAS CSCD 2026年第1期42-48,共7页
Neuromorphic devices have garnered significant attention as potential building blocks for energy-efficient hardware systems owing to their capacity to emulate the computational efficiency of the brain.In this regard,r... Neuromorphic devices have garnered significant attention as potential building blocks for energy-efficient hardware systems owing to their capacity to emulate the computational efficiency of the brain.In this regard,reservoir computing(RC)framework,which leverages straightforward training methods and efficient temporal signal processing,has emerged as a promising scheme.While various physical reservoir devices,including ferroelectric,optoelectronic,and memristor-based systems,have been demonstrated,many still face challenges related to compatibility with mainstream complementary metal oxide semiconductor(CMOS)integration processes.This study introduced a silicon-based schottky barrier metal-oxide-semiconductor field effect transistor(SB-MOSFET),which was fabricated under low thermal budget and compatible with back-end-of-line(BEOL).The device demonstrated short-term memory characteristics,facilitated by the modulation of schottky barriers and charge trapping.Utilizing these characteristics,a RC system for temporal data processing was constructed,and its performance was validated in a 5×4 digital classification task,achieving an accuracy exceeding 98%after 50 training epochs.Furthermore,the system successfully processed temporal signal in waveform classification and prediction tasks using time-division multiplexing.Overall,the SB-MOSFET's high compatibility with CMOS technology provides substantial advantages for large-scale integration,enabling the development of energy-efficient reservoir computing hardware. 展开更多
关键词 schottky barrier MOSFET back-end-of-line integration reservoir computing
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Two-terminal ferroelectric memristors:material innovation,mechanistic breakthroughs,new opportunities for intelligent computing 认领 引用
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作者 Jiaming Zhang Chaocheng Sun +4 位作者 Yan Jing Yifei Pei Jianbin Xu Xiaobing Yan Jianhui Zhao 《Science Bulletin》 SCIE EI CAS CSCD 2026年第13期3424-3452,共29页
The accelerating demand for intelligent and energy-efficient computing is driving the search for alternatives to the traditional von Neumann architecture,which is increasingly constrained by the physical separation of... The accelerating demand for intelligent and energy-efficient computing is driving the search for alternatives to the traditional von Neumann architecture,which is increasingly constrained by the physical separation of memory and processing units.Among emerging solutions,ferroelectric memristors have attracted considerable attention owing to their continuously tunable non-volatile data storage enabled by polarization-strength modulation and their electrically adjustable resistance governed by ferroelectric polarization switching.Furthermore,the long-term polarization retention of ferroelectric materials,the high endurance of the devices,and the hysteretic,accumulative,and plastic nature of polarization switching make them ideal candidates for implementing memory and neuromorphic computing paradigms.This review examines the latest advances in ferroelectric memristor research,including device physics,material systems,and ferroelectric modulation.We also highlight the neuromorphic applications of such devices in synaptic simulation and multimodal integrated perception,as well as their implementation in large-scale integrated arrays.The outlook discusses potential future directions of ferroelectric memristors for the development of high-performance and low-power intelligent systems from the perspectives of materials,devices,algorithms,and architectures.This work contributes to advancing the understanding of cutting-edge developments in ferroelectric memristors and provides a theoretical foundation and innovative insights for breakthroughs in next-generation low-power intelligent computing architectures. 展开更多
关键词 Ferroelectric memristors Ferroelectric materials Ferroelectric polarization Neuromorphic applications Intelligent computing
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Carbon nanotube-based bio-inspired neuron systems via cascaded thin-film transistor-driven light emitting diodes and optoelectronic synaptic transistors for neuromorphic computing 认领 引用
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作者 Jiaqi Li Lingzhi Wu +6 位作者 Jing Xu Min Li Mingnan Chen Chengyong Xu Shuangshuang Shao Manman Luo Jianwen Zhao 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第2期756-770,共15页
The development of bio-inspired neural systems has emerged as a transformative approach to overcome the limitations of von Neumann architecture,replicating the remarkable energy efficiency and unified sensory-processi... The development of bio-inspired neural systems has emerged as a transformative approach to overcome the limitations of von Neumann architecture,replicating the remarkable energy efficiency and unified sensory-processing capabilities of biological neurons.In this work,we present a monolithic neuromorphic platform utilizing cascaded single-walled carbon nanotube thin-film transistors(SWCNT TFTs)that integrate Mini-light-emitting diodes(Mini-LEDs)with optoelectronic synaptic transistors,achieving synergistic optoelectronic integration.The SWCNT TFTs exhibit dual functionality:(1)as highly stable active-matrix drivers(>1000 operational cycles)enabling precise Mini-LED grayscale modulation,and(2)as efficient optoelectronic synaptic devices.Fabricated at wafer-scale with micrometer feature sizes,these devices demonstrate exceptional performance metrics,including low operating voltages(±1 V),high on/off ratios(106),near-ideal subthreshold swing(78 mV·dec-1),and precise Mini-LED current regulation(10-8A-10-4A)under 25 Hz pulsed gate operation.The optoelectronic synaptic devices based on organic-semiconductor heterojunction formed between poly(3,3”’-didodecyl quaterthiophene)(PQT-12)and semiconducting SWCNTs enable broadband photoresponses(365 nm-710 nm)through efficient charge transport,driven by TFT-controlled Mini-LED pulses.The implemented bio-inspired visual system successfully emulates fundamental synaptic functionalities,exhibiting excitatory postsynaptic currents(EPSC),short-term potentiation(STP),and long-term potentiation(LTP).Notably,we demonstrate system-level functionality through a five-layer convolutional neural network,achieving 92.02%accuracy on MNIST classification,while the monolithic integration establishes a biomimetic closed-loop“electrical-optical-electrical”pathway that faithfully simulates complete biological synaptic operation.This pioneering cascade of electronic,photonic,and optoelectronic components represents a significant advancement toward high-density,energy-efficient neuromorphic computing. 展开更多
关键词 carbon nanotube thin-film transistor optoelectronic synaptic transistors bio-inspired neuron system neuromorphic computing
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Underlying Framework of All-optical Controlled Synaptic Devices for Neuromorphic Computing 认领 引用
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作者 Dunan Hu Ruqi Yang +1 位作者 Zhizhen Ye Jianguo Lu 《Nano-Micro Letters》 SCIE EI CAS CSCD 2026年第9期685-755,共71页
The rapid expansion of artificial intelligence has led to significant challenges in energy consumption and computational efficiency.To address these issues,the exploration and development of all-optical controlled(AOC... The rapid expansion of artificial intelligence has led to significant challenges in energy consumption and computational efficiency.To address these issues,the exploration and development of all-optical controlled(AOC)synaptic devices represents a promising leap forward in neuromorphic computing,offering potential solutions to the inherent limitations of traditional von Neumann architectures.AOC synaptic devices,utilizing exclusively optical signals to emulate bidirectional modulation of synaptic weights,bypass the complexity and additional energy costs associated with conventional electrical or electro-optical hybrid signals.This review articulates the underlying framework and fundamental motivations for studying AOC synapses,while systematically reviewing current research progress.We particularly highlight the synergistic relationships among physical mechanisms,material behaviors,and device architectures,as well as neuromorphic computing based on optical writing and optical erasing of information.By systematically interpreting these multidimensional correlations,we propose scalable and reproducible strategies for device design.This work will certainly herald a substantial direction of AOC synapses,providing an ideal platform for exploring neuromorphic computing for artificial intelligence. 展开更多
关键词 All-optical control Artificial synapse Device mechanisms Design framework Neuromorphic computing
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In-Sensor Reservoir Computing Employing Reconfigurable Optoelectronic Transistors for Multi-Task Learning 认领 引用
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作者 Shanshan Jiang Hainan Zhang +4 位作者 Kesheng Wang Shuo Cheng Can Fu Huanhuan Wei Gang He 《Rare Metals》 SCIE EI CAS CSCD 2026年第4期629-639,共11页
The edge deployment of artificial intelligence has driven the exploitation of compact,energy-efficient information processing systems that integrate sensing,memory,and multi-task processing functions.However,conventio... The edge deployment of artificial intelligence has driven the exploitation of compact,energy-efficient information processing systems that integrate sensing,memory,and multi-task processing functions.However,conventional vision systems suffer from significant energyime overhead,extra hardware costs,and an unaffordable algorithm.Herein,we demonstrate an in-sensor computing system employing reconfigurable optoelectronic transistors(ROETs)for multi-task learning.These transistors exhibit reconfigurable volatile and nonvolatile characteristics under both optical and electrical stimuli.Capitalizing on this reconfigurability,we establish an in-sensor reservoir computing(RC)system operating in multi-signal modes:volatile dynamics function as the reservoir,whereas nonvolatile properties configure the readout layer.The abundant optoelectronic reservoir states display exceptional feature separability and prolonged stability in the ambient atmosphere.Such a reliable RC system successfully achieves multi-task processing of images.Notably,under the optoelectronic coordination mode,it effectively alleviates feature degradation while sustaining consistently high recognition accuracy.Furthermore,the system exhibits remarkable dynamic information processing capabilities,achieving recognition accuracies of 89.02%for dynamic gestures and 96.04%for moving vehicles recognition,respectively.Supplemental functionalities,including light adaptation and image sharpening,are also implemented.This work presents a configurable multimodal platform featuring a flexible in-sensor reservoir computing architecture,providing a potential solution for efficient multi-task processing. 展开更多
关键词 in-sensor reservoir computing multi-task learning neuromorphic applications optoelectronic transistors reconfigurable devices
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Joint computation offloading and service downloading in satellite edge computing networks 认领 引用
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作者 Wu Qi Zhu Lidong 《China Communications》 SCIE EI CSCD 2026年第4期238-258,共21页
The limited onboard cache and computing resources significantly constrain the computational service capabilities of individual edge satellites in hotspot regions.To address this challenge,we propose a twotier cloud-ed... The limited onboard cache and computing resources significantly constrain the computational service capabilities of individual edge satellites in hotspot regions.To address this challenge,we propose a twotier cloud-edge computing architecture that organizes edge satellites and their associated ground clouds into multiple collaborative domains.Within each domain,we formulate a joint optimization problem for computation offloading and service downloading under the constraints of edge satellites’service deployment and caching space,aiming to minimize the sum of weighted energy consumption and latency.The originally non-convex problem is transformed into a more tractable convex optimization formulation through variable relaxation.Subsequently,we develop an alternating direction method of multipliers(ADMM)-based distributed optimization framework that enables cooperative decision-making among domain satellites for the optimization of computational offloading,service downloading,and service deleting variables.Additionally,we propose an innovative binary variable recovery algorithm that ensures feasible conversion from continuous solutions to discrete decision variables while preserving constraint satisfaction.Extensive simulations demonstrate that our approach achieves lower task execution cost and packet loss rate compared with benchmarks. 展开更多
关键词 alternating direction method of multipliers(ADMMs) cloud-edge computing architecture computation offloading edge satellites service downloading
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Heterogeneous resource allocation with latency guarantee for computing power network 认领 引用
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作者 Ailing Zhong Dapeng Wu +1 位作者 Boran Yang Ruyan Wang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第1期25-37,共13页
Computing Power Network(CPN)is a new paradigm that integrates communication,computing,and storage resources to provide services for tasks.However,tasks composed of non-independent subtasks have a preference for the re... Computing Power Network(CPN)is a new paradigm that integrates communication,computing,and storage resources to provide services for tasks.However,tasks composed of non-independent subtasks have a preference for the resources required at each stage,which increases the difficulty of heterogeneous resource allocation and reduces the latency performance of CPN services.Motivated by this,this paper jointly optimizes the full-service cycle of tasks,including transmission,task partitioning,and offloading.First,the transmission bandwidth is dynamically configured based on delay sensitivity of tasks.Second,with the real-time information from edge resource clusters and state resource clusters in the network,the optimal partitioning for a computation task is derived.Third,personalized resource allocation schemes are customized for computation and storage tasks respectively.Finally,the impact of resource parameter configuration on the latency violation probability of CPN is revealed.Moreover,compared with the benchmark schemes,our proposed scheme reduces the network latency violation probability by up to 1.17×in the same network setting. 展开更多
关键词 Latency violation probability Subtask dependencies Resource allocation Computing power network
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In-Sensor-Memory Computing for Post-Von Neumann Intelligence:A Perspective 认领 引用
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作者 Hongyu Tang Ninghai Yu +2 位作者 Pengsheng Min Ruiqian Guo Guoqi Zhang 《Nano-Micro Letters》 SCIE EI CAS CSCD 2026年第10期36-67,共32页
The rapid growth of artificial intelligence,ubiquitous sensing,and edge computing is exposing fundamental limitations of conventional von Neumann architectures,in which the physical separation of sensing,memory,and co... The rapid growth of artificial intelligence,ubiquitous sensing,and edge computing is exposing fundamental limitations of conventional von Neumann architectures,in which the physical separation of sensing,memory,and computation leads to excessive data movement,high energy consumption,and latency.As transistor scaling slows in the post-Moore era,architectural innovation has become essential to sustain progress in intelligent systems.In-sensor-memory computing(ISMC)addresses these challenges by co-locating perception,storage,and computation within unified device and system architectures,enabling in situ signal processing,mixed-signal computation,and event-driven intelligence at the data source.Recent advances in memristive and ferroelectric devices,low-dimensional and multifunctional materials,three-dimensional heterogeneous integration,and neuromorphic architectures have significantly expanded the functional scope of ISMC platforms.In parallel,the co-evolution of algorithms—including spiking neural networks,reservoir computing,and neuromorphic compilers—has facilitated the translation of device-level advantages into system-level performance.This perspective surveys the technological foundations,architectural trends,and emerging applications of ISMC,examines global industry-academia-research(IAR)collaboration,and outlines key challenges related to variability,reliability,scalability,and benchmarking.Collectively,ISMC is positioned as a post-von Neumann hardware paradigm for energy-efficient,distributed intelligence. 展开更多
关键词 In-sensor-memory computing(ISMC) Post-von Neumann intelligence Neuromorphic hardware Industry-academia-research(IAR)
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Introduction to the Special Issue on Scientific Computing and Its Application to Engineering Problems 认领 引用
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作者 Higinio Ramos M Chandru 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期22-24,共3页
Scientific computing has become a cornerstone of modern scientific discovery and engineering innovation.With the rapid advancement of computational power and numerical algorithms,problems that were once analytically i... Scientific computing has become a cornerstone of modern scientific discovery and engineering innovation.With the rapid advancement of computational power and numerical algorithms,problems that were once analytically intractable can now be studied through accurate simulations and large-scale numerical experiments.Scientific computing provides a bridge between mathematical theory,computational algorithms,and real-world engineering applications,enabling researchers to model complex phenomena such as fluid flow,structural deformation,wave propagation,stochastic processes,and nonlinear dynamical systems.The special issue entitled“Scientific Computing and Its Application to Engineering Problems”was conceived to bring together high-quality research contributions that demonstrate the role of computational mathematics in solving challenging engineering problems.The issue highlights both theoretical advances in numerical methods and their practical deployment in real-world engineering scenarios,with emphasis on robustness,computational efficiency,stability,and scalability. 展开更多
关键词 scientific computing accurate simulations fluid flows numerical algorithmsproblems computational power numerical algorithms engineering problems model complex phenomena
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基于改进Node2Vec图神经网络的轴承故障诊断 认领 引用
18
作者 谢七月 蒋朝洋 +3 位作者 宋蕾 周育才 付强 王晓丽 《噪声与振动控制》 CSCD 北大核心 2026年第3期90-96,169,共7页
针对传统滚动轴承故障诊断方法复杂、准确率不高且抗噪能力不足的问题,提出一种基于改进图嵌入的图神经网络轴承故障诊断方法。该方法通过对Node2Vec算法进行改进,在对节点邻居点进行随机游走时,加入基于邻居点在图中的权重信息的游走策... 针对传统滚动轴承故障诊断方法复杂、准确率不高且抗噪能力不足的问题,提出一种基于改进图嵌入的图神经网络轴承故障诊断方法。该方法通过对Node2Vec算法进行改进,在对节点邻居点进行随机游走时,加入基于邻居点在图中的权重信息的游走策略,使得模型能够更有针对性捕捉节点关系信息,有效避免传统的图嵌入没有充分考虑边缘交互信息的问题。同时引入对偶图的概念,通过对偶图的构建和数据嵌入,进一步挖掘数据中的信息。实验结果表明,所提出的方法对于凯斯西储大学轴承数据集在无噪声,以及噪声为-5、-10 dB的环境条件下均有较高的诊断准确率;并基于PT500 mini轴承故障试验台进行验证,实验准确率为97.5%,展现了该方法在不同工况下和不同工业环境中的实际应用潜力。 展开更多
关键词 故障诊断 滚动轴承故障 Node2Vec算法 对偶图 图神经网络
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Toward high-layer 3D hafnia ferroelectric stacks for neuromorphic computing:manufacturing insights and integration challenges 认领 引用
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作者 Ruifu Zhou Hyeon-seo Do Jang-Sik Lee 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第3期396-429,共34页
Ferroelectric hafnium-oxide(HfO2)films have revitalized interest in brain-inspired hardware because of their high scalability,compatibility with complementary metal-oxide-semiconductor(CMOS)processes,and suitability f... Ferroelectric hafnium-oxide(HfO2)films have revitalized interest in brain-inspired hardware because of their high scalability,compatibility with complementary metal-oxide-semiconductor(CMOS)processes,and suitability for three-dimensional(3D)architectures.This review first analyses the origin,deposition routes,and performance of hafnia-based devices,including ferroelectric field-effect transistor,ferroelectric tunnelling junction and ferroelectric capacitor.As artificial intelligence(AI)continues to advance,the demand for higher memory density becomes increasingly critical.This review presents hafnia-based devices and arrays in both planar and 3D architectures.In 3D structures,the review discusses the principal integration constraints—back-end-of-line(BEOL)-compatible crystallization,conformal atomic layer deposition(ALD)with controlled phase and defects in high-aspect-ratio features,and cross-layer stress together with layer-to-layer variability/disturbance,which collectively determine stackable scalability and influence energy efficiency and training stability,thereby pointing toward compact,energy-efficient,and scalable 3D neuromorphic hardware based on hafnia ferroelectrics. 展开更多
关键词 hafnium oxide ferroelectrics neuromorphic computing 3D integration
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