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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
1
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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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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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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Back-gate-tuned organic electrochemical transistor with temporal dynamic modulation for reservoir computing 认领 引用
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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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Introduction to the Special Issue on Next-Generation Intelligent Networks and Systems:Advances in IoT,Edge Computing,and Secure Cyber-Physical Applications 认领 引用
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作者 Nishu Gupta Manuel J.C.S.Reis 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期25-28,共4页
The accelerating convergence of intelligent networking paradigms,data-driven modeling,and cyberphysical integration is reshaping the foundations of modern engineering systems.Within this context,this Special Issue of ... The accelerating convergence of intelligent networking paradigms,data-driven modeling,and cyberphysical integration is reshaping the foundations of modern engineering systems.Within this context,this Special Issue of Computer Modeling in Engineering&Sciences(CMES)is devoted to recent advances in next-generation intelligent networks and systems,with a particular emphasis on the synergistic roles of the Internet of Things(IoT),edge computing,and secure cyber-physical applications. 展开更多
关键词 intelligent networking paradigmsdata driven computer modeling internet things edge computing intelligent networking paradigms cyberphysical integration internet things iot edge computingand data driven modeling
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Quantum computing-enhanced topology optimization with stress constraints for truss structures 认领 引用 被引量：2
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作者 Yan Wang Dixiong Yang +1 位作者 Zhenzeng Lei Guohai Chen 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第6期41-57,共17页
Quantum computing,leveraging the properties of quantum physics such as quantum superposition and entanglement,possesses the potential for exponential acceleration compared to classical computing.It can significantly e... Quantum computing,leveraging the properties of quantum physics such as quantum superposition and entanglement,possesses the potential for exponential acceleration compared to classical computing.It can significantly enhance solution efficiency in topology optimization and effectively avoid the entrapment in local optima.This paper proposes a hybrid classical-quantum computing framework to solve the stress-constrained topology optimization problem for truss structures.Initially,structural analyses are performed on a classical computer to determine the stresses of truss members.Then,the optimization problem is formulated through incremental updates of member cross-sectional areas to make it compatible with a quantum annealer.The update strategy consists of a directional-control function and a magnitude-control function.By embedding stress constraints directly into the directional-control function,the original optimization problem is reformulated as a quadratic unconstrained binary optimization model suitable for quantum annealing.To realize a balance between solution accuracy and iteration efficiency,a dynamic strategy for adjusting the magnitude of area increments is proposed.Thus,the quantum annealer can effectively achieve the optimal solutions.When only the access time of the quantum processing unit is considered,the results from 2D and 3D examples of truss topology optimization validate the effectiveness of the proposed framework,and demonstrate the great potential of quantum computing in structural optimization. 展开更多
关键词 Topology optimization Truss structures Quantum computing Quantum annealing algorithm Quadratic unconstrained binary optimization problem
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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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In-Sensor-Memory Computing for Post-Von Neumann Intelligence:A Perspective 认领 引用 被引量：1
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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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Intelligent Reconfigurable Skyrmion-Based Multi-Port Logic Device for In-Memory Computing 认领 引用
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作者 Fuhao Zou Ziyuan Liu +3 位作者 Zijing Zhao Muhammad Humayun Chundong Wang Xiaolei Wang 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第3期331-345,共15页
New electronic devices based on the physical properties of electrically driven skyrmions are promising for logic computing and nonvolatile memory applications.However,achieving efficient and practical compute-storage ... New electronic devices based on the physical properties of electrically driven skyrmions are promising for logic computing and nonvolatile memory applications.However,achieving efficient and practical compute-storage integration remains challenging owing to the structural complexity,limited functionality,and low flexibility observed in most skyrmion-based devices.In this study,we designed a novel device architecture that integrates seven basic logic gates into a unified physical structure.Their operation can be enabled by physical mechanisms,such as spin-orbit torque,spin-transfer torque,skyrmion-edge repulsions,and skyrmion-skyrmion interactions.Furthermore,by incorporating voltage-controlled magnetic anisotropy,the device achieved multi-input capability and reconfigurability functionality.Ultralow power consumption(<1 fJ/bit per logic function)and extremely high logic density were achieved.Significantly,the compatibility of this nanotrack design with existing skyrmion racetrack memory paves the way for advanced in-memory computing in spintronic architectures. 展开更多
关键词 voltage controlled magnetic anisotropy intelligent reconfigurable skyrmion based multi port logic device memory computing logic computing device architecture spin transfer torque spin orbit torque integrates seven basic logic gates
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Self-Rectifying Memristors for Beyond-CMOS Computing:Mechanisms,Materials,and Integration Prospects 认领 引用
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作者 Guobin Zhang Xuemeng Fan +8 位作者 Zijian Wang Pengtao Li Zhejia Zhang Bin Yu Dawei Gao Desmond Loke Shuai Zhong Qing Wan Yishu Zhang 《Nano-Micro Letters》 SCIE EI CAS CSCD 2026年第6期293-335,共43页
The deceleration of Moore's law and the energy–latency drawbacks of the von Neumann bottleneck have heightened the pursuit for beyond-CMOS designs that integrate memory and compute.Self-rectifying memristors(SRMs... The deceleration of Moore's law and the energy–latency drawbacks of the von Neumann bottleneck have heightened the pursuit for beyond-CMOS designs that integrate memory and compute.Self-rectifying memristors(SRMs)have emerged as promising building blocks for high-performance,low-power systems by combining resistive switching with intrinsic diode-like behavior.Their unidirectional conduction inhibits sneak-path currents in crossbar arrays devoid of external selectors,while nonlinear I–V characteristics,adjustable conductance states,low operating voltages,and rapid switching facilitate efficient vector–matrix operations,neuromorphic plasticity,and hardware security primitives.This review synthesizes the working mechanisms of SRMs,surveys material,and structural strategies and compares device metrics relevant to array-scale deployment(rectification ratio,nonlinearity,endurance,retention,variability,and operating voltage).We assess SRM-enabled in-memory computing and neuromorphic applications,as well as security functions such as physical unclonable functions and reconfigurable cryptographic primitives.Integration pathways toward CMOS compatibility are analyzed,including back-end-of-line thermal budgets,uniformity,write disturb mitigation,and reliability.Finally,we outline key challenges and opportunities:materials/architecture co-design,precision analog training,stochasticity control/exploitation,3D stacking,and standardized benchmarking that can accelerate large-scale SRM adoption.Through the use of specialized materials and structural optimization,SRMs are set to provide selector-free,densely integrated,and energy-efficient hardware for future information processing. 展开更多
关键词 Self-rectifying memristor Beyond-CMOS CMOS compatibility In-memory computing Neuromorphic computing
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Energy Aware Task Scheduling of IoT Application Using a Hybrid Metaheuristic Algorithm in Cloud Computing 认领 引用
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作者 Ahmed Awad Mohamed Eslam Abdelhakim Seyam +4 位作者 Ahmed R.Elsaeed Laith Abualigah Aseel Smerat Ahmed M.AbdelMouty Hosam E.Refaat 《Computers, Materials & Continua》 SCIE EI 2026年第3期1786-1803,共18页
In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task schedul... In recent years,fog computing has become an important environment for dealing with the Internet of Things.Fog computing was developed to handle large-scale big data by scheduling tasks via cloud computing.Task scheduling is crucial for efficiently handling IoT user requests,thereby improving system performance,cost,and energy consumption across nodes in cloud computing.With the large amount of data and user requests,achieving the optimal solution to the task scheduling problem is challenging,particularly in terms of cost and energy efficiency.In this paper,we develop novel strategies to save energy consumption across nodes in fog computing when users execute tasks through the least-cost paths.Task scheduling is developed using modified artificial ecosystem optimization(AEO),combined with negative swarm operators,Salp Swarm Algorithm(SSA),in order to competitively optimize their capabilities during the exploitation phase of the optimal search process.In addition,the proposed strategy,Enhancement Artificial Ecosystem Optimization Salp Swarm Algorithm(EAEOSSA),attempts to find the most suitable solution.The optimization that combines cost and energy for multi-objective task scheduling optimization problems.The backpack problem is also added to improve both cost and energy in the iFogSim implementation as well.A comparison was made between the proposed strategy and other strategies in terms of time,cost,energy,and productivity.Experimental results showed that the proposed strategy improved energy consumption,cost,and time over other algorithms.Simulation results demonstrate that the proposed algorithm increases the average cost,average energy consumption,and mean service time in most scenarios,with average reductions of up to 21.15%in cost and 25.8%in energy consumption. 展开更多
关键词 Energy-efficient tasks internet of things(IoT) cloud fog computing artificial ecosystem-based optimization salp swarm algorithm cloud computing
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Heterogeneous Computing Power Scheduling Method Based on Distributed Deep Reinforcement Learning in Cloud-Edge-End Environments 认领 引用
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作者 Jinwei Mao Wang Luo +5 位作者 Jiangtao Xu Daohua Zhu WeiLiang Zhechen Huang Bao Feng Shuang Yang 《Computers, Materials & Continua》 SCIE EI 2026年第5期1964-1985,共22页
With the rapid development of power Internet of Things(IoT)scenarios such as smart factories and smart homes,numerous intelligent terminal devices and real-time interactive applications impose higher demands on comput... With the rapid development of power Internet of Things(IoT)scenarios such as smart factories and smart homes,numerous intelligent terminal devices and real-time interactive applications impose higher demands on computing latency and resource supply efficiency.Multi-access edge computing technology deploys cloud computing capabilities at the network edge;constructs distributed computing nodes and multi-access systems and offers infrastructure support for services with low latency and high reliability.Existing research relies on a strong assumption that the environmental state is fully observable and fails to thoroughly consider the continuous time-varying features of edge server load fluctuations,leading to insufficient adaptability of the model in a heterogeneous dynamic environment.Thus,this paper establishes a framework for end-edge collaborative task offloading based on a partially observable Markov decision-making process(POMDP)and proposes a method for end-edge collaborative task offloading in heterogeneous scenarios.It achieves time-series modeling of the historical load characteristics of edge servers and endows the agent with the ability to be aware of the load in dynamic environmental states.Moreover,by dynamically assessing the exploration value of historical trajectories in the central trajectory pool and adjusting the sample weight distribution,directional exploration and strategy optimization of high-value trajectories are realized.Experimental results indicate that the proposed method exhibits distinct advantages compared with existing methods in terms of average delay and task failure rate and also verifies the method’s robustness in a dynamic environment. 展开更多
关键词 Edge computing end-edge collaboration heterogeneous computing power scheduling resource allocation
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Virtual QPU:A Novel Implementation of Quantum Computing 认领 引用
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作者 Danyang Zheng Jinchen Xv +1 位作者 Xin Zhou Zheng Shan 《Computers, Materials & Continua》 SCIE EI 2026年第4期1008-1029,共22页
The increasing popularity of quantum computing has resulted in a considerable rise in demand for cloud quantum computing usage in recent years.Nevertheless,the rapid surge in demand for cloud-based quantum computing r... The increasing popularity of quantum computing has resulted in a considerable rise in demand for cloud quantum computing usage in recent years.Nevertheless,the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity.In order to meet the needs of an increasing number of researchers,it is imperative to facilitate efficient and flexible access to computing resources in a cloud environment.In this paper,we propose a novel quantum computing paradigm,Virtual QPU(VQPU),which addresses this issue and enhances quantum cloud throughput with guaranteed circuit fidelity.The proposal introduces three innovative concepts:(1)The integration of virtualization technology into the field of quantum computing to enhance quantum cloud throughput.(2)The introduction of an asynchronous execution of circuits methodology to improve quantum computing flexibility.(3)The development of a virtual QPU allocation scheme for quantum tasks in a cloud environment to improve circuit fidelity.The concepts have been validated through the utilization of a self-built simulated quantum cloud platform. 展开更多
关键词 Quantum computing scheduling parallel computing computational paradigm
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An Efficient Hierarchical Resource Scheduling in Terminal-Side Computing Power Networks 认领 引用
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作者 Wang Hengjiang Cui Fang +2 位作者 Ni Mao Li Chao Tao Xiaoming 《China Communications》 SCIE EI CSCD 2026年第4期227-237,共11页
To address the critical challenges of nonuniform resource sensing and high dynamism within terminal-side computing power networks,this paper proposes a novel and efficient hierarchical resource scheduling mechanism.Fi... To address the critical challenges of nonuniform resource sensing and high dynamism within terminal-side computing power networks,this paper proposes a novel and efficient hierarchical resource scheduling mechanism.Firstly,architect a collaborative network architecture integrating a terminal layer and a cloud layer.Subsequently,a multi-dimensional model for computing power sensing and standardized measurement is established.Furthermore,investigate a hierarchical scheduling mechanism based on federated learning,which facilitates the effective management and intelligent scheduling of heterogeneous,dynamic resources.Experimental results demonstrate that this mechanism significantly reduces service latency in near-field computing,terminal-cloud collaboration,and ubiquitous computing scenarios. 展开更多
关键词 computing power networks resource scheduling computing power sensing
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Quantum-enhanced reconfigurable in-memory stochastic computing 认领 引用
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作者 Hong-Zhe Yang Jian-Peng Dou +5 位作者 Feng Lu Xiao-Wen Shang Chao-Ni Zhang Heng Zhou Hao Tang Xian-Min Jin 《Light: Science & Applications》 SCIE EI CAS CSCD 2026年第6期1974-1984,共11页
In-memory computing,which enables computation directly within memory,represents an efficient approach to processing massively parallel computation tasks that are intractable for conventional computers.However,implemen... In-memory computing,which enables computation directly within memory,represents an efficient approach to processing massively parallel computation tasks that are intractable for conventional computers.However,implementations of in-memory computing have been primarily limited to the classical regime,with its nonclassical counterpart yet to be fully explored.Quantum memory,with its unique capability to generate,preserve,and nontrivially operate on quantum states,offers spectacular quantum-enhanced advantages and is thus a promising candidate for in-memory computing.Here,leveraging a room-temperature quantum memory,we demonstrate a quantum-enhanced and reconfigurable in-memory stochastic computing system,where correlated photons,randomly produced in the quantum memory,serve as the computing resources.We show that addition and multiplication operations can be straightforwardly achieved by accumulating photon counts,and multiple computing tasks can be accelerated by mapping them into parallel accumulations of photon counts.Furthermore,the calculation results are obtained through stochastic processes,ensuring security in remote computation since no efficient information can be distinguished by eavesdropping on a small portion of the computation data.This in-memory computing system is enhanced by nonclassical correlations,which accelerate computing process and may stimulate future research and applications in the emerging field of quantum-enhanced computing architectures. 展开更多
关键词 nonclassical correlations conventional computershoweverimplementations quantum enhanced computing reconfigurable memory computing computation directly within memoryrepresents processing massively parallel computation tasks quantum memory remote computation security
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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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Efficient Spin-Orbit Torques Enabled by Vanadium-Induced Orbital Currents for Neuromorphic Computing 认领 引用
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作者 Zhonghai Yu Yaohui Du +15 位作者 Mengyang Yan Pengnan Zhao Rui Hou Keqin Li Lihuan Yang Kaiwei Guo Bingyue Bian Zhengyu Xiao Lei Cheng Hongru Wang Jia-Min Lai Zhiyong Quan Dongsheng Yang Yakun Liu Fei Wang Xiaohong Xu 《Rare Metals》 SCIE EI CAS CSCD 2026年第7期363-371,共9页
Spin-orbit torque(SOT)provides an efficient electrical pathway for encoding spin states and underpins emerging,ultrafast,and nonvolatile spintronic memories and logic devices.Reducing the switching current density and... Spin-orbit torque(SOT)provides an efficient electrical pathway for encoding spin states and underpins emerging,ultrafast,and nonvolatile spintronic memories and logic devices.Reducing the switching current density and power consumption remains a central challenge for practical applications.Here,we demonstrate that a light metal,vanadium(V),can serve as an efficient orbital-current source to substantially enhance SOT efficiency.In V/Pt/CoFeB heterostructures,orbital currents generated in V are effectively converted into spin currents by the spin-orbit-active Pt layer,giving rise to enhanced torques acting on the perpendicularly magnetized CoFeB layer.As a result,the critical switching current density and power are reduced by 54%and 27%,respectively,compared with conventional spin current-dominated Pt/CoFeB structures.Moreover,V/Pt/CoFeB-based devices further enable neuromorphic computing functionalities,achieving a handwritten digit recognition accuracy of 89%.These results highlight orbital-current engineering as a viable strategy for realizing low-power SOT devices and advancing spintronic hardware for neuromorphic computing. 展开更多
关键词 neuromorphic computing orbital current orbital torque spin-orbit torque switching of perpendicular magnetization
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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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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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