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Exploring reservoir computing:Implementation via double stochastic nanowire networks

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Chinese Physics B 2024年第3期33卷 572-582页

作者：唐健峰夏磊李广隶付军段书凯王丽丹 College of Artificial Intelligence Southwest UniversityChongqing 400715China Brain-inspired computing & Intelligent Control of Chongqing Key Laboratory Chongqing 400715China National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing 400715China Chongqing Brain Science Collaborative Innovation Center Chongqing 400715China Key Laboratory of Luminescence Analysis and Molecular Sensing Ministry of EducationSouthwest UniversityChongqing 400715China State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing 400715China

Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data analysis.This paper presents a model based on these nanowire networks,with an improved conductance variation profile.We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses.The nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction analysis.Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction accuracy.Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing.

关键词： double-layer stochastic(DS)nanowire network architecture neuromorphic computation nanowire network reservoir computing time series prediction

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Experimental and numerical demonstration of hierarchical time-delay reservoir computing based on cascaded VCSELs with feedback and multiple injections

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Science China(Information Sciences) 2024年第2期67卷 279-290页

作者： Xingxing GUO Shuiying XIANG Xingyu CAO Biling GU State Key Laboratory of Integrated Service Networks Xidian University State Key Discipline Laboratory of Wide Bandgap Semiconductor Technology School of MicroelectronicsXidian University

In this paper, we propose and demonstrate experimentally and numerically a hierarchical timedelay optical reservoir computing(RC) system based on cascaded vertical-cavity surface-emitting lasers(VCSELs) with feedback and multiple injections. The prediction performance characteristics of the hierarchical time-delay RC system based on cascaded VCSELs under different reservoir layers are compared.Evidently, the prediction performance of the hierarchical time-delay RC system is first improved and saturates as the number of reservoir layers increases. Besides, the impacts of key factors on predicting the hierarchical time-delay RC system are also analyzed in detail experimentally and numerically. This proposed hierarchical time-delay RC system based on VCSELs is useful for the further development of RC systems and may be beneficial to improve the ability of RC systems to solve more complex problems.

关键词： vertical-cavity surface-emitting laser reservoir computing neuromorphic photonics time-delay system information processing

A multi-terminal ion-controlled transistor with multifunctionality and wide temporal dynamics for reservoir computing

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Nano Research 2024年第5期17卷 4444-4453页

作者： Kekang Liu Jie Li Fangzhou Li Yiyuan Lin Hongrui Liu Linzi Liang Zhiyuan Luo Wei Liu Mengye Wang Feichi Zhou Yanghui Liu School of Materials Sun Yat-Sen UniversityShenzhen 518107China School of Microelectronics Southern University of Science and TechnologyShenzhen 518055China School of Software East China Jiaotong UniversityNanchang 330013China

reservoir computing(RC)is an energy-efficient computational framework with low training cost and high efficiency in processing spatiotemporal information.The state-of-the-art fully memristor-based hardware RC system suffers from bottlenecks in the computation efficiencies and accuracy due to the limited temporal tunability in the volatile memristor for the reservoir layer and the nonlinearity in the nonvolatile memristor for the readout layer.Additionally,integrating different types of memristors brings fabrication and integration complexities.To overcome the challenges,a multifunctional multi-terminal electrolyte-gated transistor(MTEGT)that combines both electrostatic and electrochemical doping mechanisms is proposed in this work,integrating both widely tunable volatile dynamics with high temporal tunable range of 10^(2) and nonvolatile memory properties with high long-term potentiation/long-term depression(LTP/LTD)linearity into a single device.An ion-controlled physical RC system fully implemented with only one type of MTEGT is constructed for image recognition using the volatile dynamics for the reservoir and nonvolatility for the readout layer.Moreover,an ultralow normalized mean square error of 0.002 is achieved in a time series prediction task.It is believed that the MTEGT would underlie next-generation neuromorphic computing systems with low hardware costs and high computational performance.

关键词： reservoir computing multi-terminal electrolyte-gated transistor ionic controlling rich dynamics nonlinear dynamical prediction

Emerging memristors and applications in reservoir computing

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Frontiers of physics 2024年第1期19卷 219-239页

作者： Hao Chen Xin-Gui Tang Zhihao Shen Wen-Tao Guo Qi-Jun Sun Zhenhua Tang Yan-Ping Jiang School of Physics and Optoelectronic Engineering Guangdong University of TechnologyGuangzhou Higher Education Mega CenterGuangzhou 510006China

Recently, with the emergence of ChatGPT, the field of artificial intelligence has garnered widespread attention from various sectors of society. reservoir computing (RC) is a neuromorphic computing algorithm used to analyze time-series data. Unlike traditional artificial neural networks that require the weight values of all nodes in the trained network, RC only needs to train the readout layer. This makes the training process faster and more efficient, and it has been used in various applications, including speech recognition, image classification, and control systems. Its flexibility and efficiency make it a popular choice for processing large amounts of complex data. A recent research trend is to develop physical RC, which utilizes the nonlinear dynamic and short-term memory properties of physical systems (photonic modules, spintronic devices, memristors, etc.) to construct a fixed random neural network structure for processing input data to reduce computing time and energy. In this paper, we introduced the recent development of memristors and demonstrated the remarkable data processing capability of RC systems based on memristors. Not only do they possess excellent data processing ability comparable to digital RC systems, but they also have lower energy consumption and greater robustness. Finally, we discussed the development prospects and challenges faced by memristors-based RC systems.

关键词： reservoir computing memristor resistive switching artificial synapse neuromorphic computing

In-materio reservoir computing based on nanowire networks:fundamental,progress,and perspective

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Materials Futures 2023年第2期2卷 79-93页

作者： Renrui Fang Woyu Zhang Kuan Ren Peiwen Zhang Xiaoxin Xu Zhongrui Wang Dashan Shang State Key Laboratory of Fabrication Technologies for Integrated Circuits Institute of Microelectronics of Chinese Academy of SciencesBeijing 100029People’s Republic of China Department of Electrical and Electronic Engineering The University of Hong KongHong Kong 999077People’s Republic of China University of Chinese Academy of Sciences Beijing 100049People’s Republic of China

The reservoir computing(RC)system,known for its ability to seamlessly integrate memory and computing functions,is considered as a promising solution to meet the high demands for time and energy-efficient computing in the current big data landscape,compared with traditional silicon-based computing systems that have a noticeable disadvantage of separate storage and computation.This review focuses on in-materio RC based on nanowire networks(NWs)from the perspective of materials,extending to reservoir devices and applications.The common methods used in preparing nanowires-based reservoirs,including the synthesis of nanowires and the construction of networks,are firstly systematically summarized.The physical principles of memristive and memcapacitive junctions are then explained.Afterwards,the dynamic characteristics of nanowires-based reservoirs and their computing capability,as well as the neuromorphic applications of NWs-based RC systems in recognition,classification,and forecasting tasks,are explicated in detail.Lastly,the current challenges and future opportunities facing NWs-based RC are highlighted,aiming to provide guidance for further research.

关键词： reservoir computing nanowire networks short-term memory dynamic characteristics neuromorphic computing

A brief review of integrated and passive photonic reservoir computing systems and an approach for achieving extra non-linearity in passive devices

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Science China(Information Sciences) 2020年第6期63卷 97-104页

作者： Dachuan WU Yasha YI Yuxiao ZHANG Integrated Nano Optoelectronics Laboratory Department of Electrical and Computer Engineering University of Michigan Energy Institute University of Michigan

Photonic-based reservoir computing(RC) systems have attracted significant attention. Integrated and purely passive systems are compatible with complementary metal-oxide-semiconductor devices,but are limited by the lack of non-linear components. This study consists of two parts: firstly, a review on the published integrated and passive RC system is presented. The review focuses on the structural configuration(rather than the mathematical model) of the neural network; secondly, a new approach for achieving an integrated and passive photonic RC system is introduced and discussed. This approach employs a mode combiner in front of the reservoir to achieve an extra non-linearity in a purely passive device. Moreover,the approach is numerically investigated, and an XOR(exclusive or) task is used to test the device, and the result shows that the new approach satisfies the requirement of an RC system.

关键词： reservoir computing integrated photonics neural network non-linearity

Direct laser writing of graphene oxide for ultra-low power consumption memristors in reservoir computing for digital recognition

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National Science Open 2022年第3期1卷 46-58页

作者： Min Chen Zhengfen Wan Hao Dong Qinyu Chen Min Gu Qiming Zhang Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200090China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200090China

A memristor is a promising candidate of new electronic synaptic devices for neuromorphic computing.However,conventional memristors often exhibit complex device structures,cumbersome manufacturing processes,and high energy consumption.Graphene-based materials show great potential as the building materials of memristors.With direct laser writing technology,this paper proposes a lateral memristor with reduced graphene oxide(rGO)and Pt as electrodes and graphene oxide(GO)as function material.This Pt/GO/rGO memristor with a facile lateral structure can be easily fabricated and demonstrates an ultra-low energy consumption of 200 nW.Typical synaptic behaviors are successfully emulated.Meanwhile,the Pt/GO/rGO memristor array is applied in the reservoir computing network,performing the digital recognition with a high accuracy of 95.74%.This work provides a simple and low-cost preparation method for the massive production of artificial synapses with low energy consumption,which will greatly facilitate the development of neural network computing hardware platforms.

关键词： direct laser writing memristor array graphene oxide reservoir computing

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Performance Enhancement of reservoir computing with Multi-Clustered Structure

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Performance Enhancement of Reservoir Computing with Multi-Cl...

Analog Optical computing for Artificial Intelligence

第37届中国控制会议

作者： Zheng Xu Anguo Zhang Wei Zhu School of Mechanical Engineering Nanjing University of Science and Technology Research Institute of Ruijie Ruijie Networks Co. Ltd.

reservoir computing（RC） is an attractive model of recurrent neural networks. In RC framework, a sparse interconnected hidden layer called reservoir, is the only training-required component that the simple linear regression method is capable to train it. This advantage makes the RC easier to implement and apply to engineering application than other RNNs.Recently, many studies have been focused on optimizing the performance of RC via learning algorithms or topological structures, which have been demonstrated critical to RC. In this paper, we propose a novel reservoir network with multi-clustered structure which is organized according to a developmental time windows algorithm. Experimental results showed that the model can significantly reduce the computational errors and standard deviation of the errors on the numeric prediction benchmark of Mackey-Glass chaotic time series. Moreover, we found that there exist the optimal values of the cluster size and time window size, where RC reaches the best performance in terms of computational error. And interestingly, in this case, the reservoir network exhibits the largest Shannon information entropy of neuronal activity patterns, indicating that information can be effectively transmitted in neural networks with complex dynamics. Our results showed that the multi-clustered generating method effectively promote the computational capability of reservoir computing.

关键词： reservoir computing Multi-cluster structure Information entropy

来源： cnki会议评论

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Engineering 2022年第3期8卷 133-145页

作者： Jiamin Wu Xing Lin Yuchen Guo Junwei Liu Lu Fang Shuming Jiao Qionghai Dai Department of Automation Tsinghua UniversityBeijing 100084China Institute for Brain and Cognitive Science Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Beijing Innovation Center for Future Chips Tsinghua UniversityBeijing 100084China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China Department of Physics The Hong Kong University of Science and TechnologyHong Kong 999077China Peng Cheng Laboratory Shenzhen 518055China

The rapid development of artificial intelligence(AI)facilitates various applications from all areas but also poses great challenges in its hardware implementation in terms of speed and energy because of the explosive growth of data.Optical computing provides a distinctive perspective to address this bottleneck by harnessing the unique properties of photons including broad bandwidth,low latency,and high energy efficiency.In this review,we introduce the latest developments of optical computing for different AI models,including feedforward neural networks,reservoir computing,and spiking neural networks(SNNs).Recent progress in integrated photonic devices,combined with the rise of AI,provides a great opportunity for the renaissance of optical computing in practical applications.This effort requires multidisciplinary efforts from a broad community.This review provides an overview of the state-of-the-art accomplishments in recent years,discusses the availability of current technologies,and points out various remaining challenges in different aspects to push the frontier.We anticipate that the era of large-scale integrated photonics processors will soon arrive for practical AI applications in the form of hybrid optoelectronic frameworks.

关键词： Artificial intelligence Optical computing Opto-electronic framework Neural network Neuromorphic computing reservoir computing Photonics processor

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