scholarly journals Evolutionary 2D organic crystals for optoelectronic transistors and neuromorphic computing

2022 ◽  
Author(s):  
Fangsheng Qian ◽  
Xiaobo Bu ◽  
Junjie Wang ◽  
Ziyu Lv ◽  
Su-Ting Han ◽  
...  
Keyword(s):  
Data Storage ◽  
Nonvolatile Memory ◽  
Wide Spectrum ◽  
Organic Crystals ◽  
Algorithm Optimization ◽  
Neuromorphic Computing ◽  
Memory Circuit ◽  
Computing Paradigm ◽  
Free Interfaces ◽  
Shielding Effects

Abstract Brain-inspired neuromorphic computing has been extensively researched, taking advantage of increased computer power, the acquisition of massive data, and algorithm optimization. Neuromorphic computing requires mimicking synaptic plasticity and enables near-in-sensor computing. In synaptic transistors, how to elaborate and examine the link between microstructure and characteristics is a major difficulty. Due to the absence of interlayer shielding effects, defect-free interfaces, and wide spectrum responses, reducing the thickness of organic crystals to the 2D limit has a lot of application possibilities in this computing paradigm. This paper presents an update on the progress of 2D organic crystal-based transistors for data storage and neuromorphic computing. The promises and synthesis methodologies of 2D organic crystals are summarized. Following that, applications of 2D organic crystals for ferroelectric nonvolatile memory, circuit-type optoelectronic synapses, and neuromorphic computing are addressed. Finally, new insights and challenges for the field's future prospects are presented, pushing the boundaries of neuromorphic computing even farther.

scholarly journals Neuro-Inspired Signal Processing in Ferromagnetic Nanofibers

Biomimetics ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 32
Author(s):  
Tomasz Blachowicz ◽  
Jacek Grzybowski ◽  
Pawel Steblinski ◽  
Andrea Ehrmann
Keyword(s):  
Data Processing ◽  
Data Storage ◽  
Domain Walls ◽  
Data Transfer ◽  
Synaptic Activity ◽  
Neuromorphic Computing ◽  
Rotating Magnetic Fields ◽  
Micromagnetic Simulations ◽  
Energy Consuming ◽  
Stochastic Data

Computers nowadays have different components for data storage and data processing, making data transfer between these units a bottleneck for computing speed. Therefore, so-called cognitive (or neuromorphic) computing approaches try combining both these tasks, as is done in the human brain, to make computing faster and less energy-consuming. One possible method to prepare new hardware solutions for neuromorphic computing is given by nanofiber networks as they can be prepared by diverse methods, from lithography to electrospinning. Here, we show results of micromagnetic simulations of three coupled semicircle fibers in which domain walls are excited by rotating magnetic fields (inputs), leading to different output signals that can be used for stochastic data processing, mimicking biological synaptic activity and thus being suitable as artificial synapses in artificial neural networks.

scholarly journals Computer Data Storage and Management Platform Based on Big Data

2021 ◽  
Vol 2066 (1) ◽  
pp. 012022
Author(s):  
Cheng Luo
Keyword(s):  
Big Data ◽  
Data Management ◽  
Performance Improvement ◽  
Data Storage ◽  
Nonvolatile Memory ◽  
Linear Structure ◽  
Ring Structure ◽  
Computer Data ◽  
Management Platform ◽  
A Performance

Abstract Due to the continuous development of information technology, data has increasingly become the core of the daily operation of enterprises and institutions, the main basis for decision-making development. At the same time, due to the development of network, the storage and management of computer data has attracted more and more attention. Aiming at the common problems of computer data storage and management in practical work, this paper analyzes the object and content of data management, investigates the situation of computer data storage and management in China in recent two years, and interviews and tests the data of programming in this design platform. At the same time, in view of the related problems, the research results are applied to practice. On the basis of big data, the storage and management platform is designed. The research and design adopts a special B+ tree node linear structure of CIRC tree, and the linear node structure is changed into a ring structure, which greatly reduces the number of data persistence instructions and the performance overhead. The results show that compared with the most advanced B+ tree design for nonvolatile memory, crab tree has 3.1 times and 2.5 times performance improvement in reading and writing, respectively. Compared with the previous NV tree designed for nonvolatile memory, it has a performance improvement of 1.5 times, and a performance improvement of 8.4 times compared with the latest fast-fair. In the later stage, the expansion of the platform functions is conducive to the analysis and construction of data related storage and management functions, and further improve the ability of data management.

scholarly journals Ionic Sieving Through One‐Atom‐Thick 2D Material Enables Analog Nonvolatile Memory for Neuromorphic Computing

Small ◽  
2021 ◽  
pp. 2103543
Author(s):  
Revannath Dnyandeo Nikam ◽  
Jongwon Lee ◽  
Wooseok Choi ◽  
Writam Banerjee ◽  
Myonghoon Kwak ◽  
...  
Keyword(s):  
Nonvolatile Memory ◽  
Neuromorphic Computing ◽  
2D Material

scholarly journals Microservice Security Agent Based On API Gateway in Edge Computing

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4905 ◽  
Author(s):  
Rongxu Xu ◽  
Wenquan Jin ◽  
Dohyeun Kim
Keyword(s):  
Data Storage ◽  
User Authentication ◽  
Edge Computing ◽  
Security And Privacy ◽  
Agent Based ◽  
Computing Platform ◽  
Authentication Mechanism ◽  
Computing Paradigm ◽  
Security Agent ◽  
Secure Authentication

Internet of Things (IoT) devices are embedded with software, electronics, and sensors, and feature connectivity with constrained resources. They require the edge computing paradigm, with modular characteristics relying on microservices, to provide an extensible and lightweight computing framework at the edge of the network. Edge computing can relieve the burden of centralized cloud computing by performing certain operations, such as data storage and task computation, at the edge of the network. Despite the benefits of edge computing, it can lead to many challenges in terms of security and privacy issues. Thus, services that protect privacy and secure data are essential functions in edge computing. For example, the end user’s ownership and privacy information and control are separated, which can easily lead to data leakage, unauthorized data manipulation, and other data security concerns. Thus, the confidentiality and integrity of the data cannot be guaranteed and, so, more secure authentication and access mechanisms are required to ensure that the microservices are exposed only to authorized users. In this paper, we propose a microservice security agent to integrate the edge computing platform with the API gateway technology for presenting a secure authentication mechanism. The aim of this platform is to afford edge computing clients a practical application which provides user authentication and allows JSON Web Token (JWT)-based secure access to the services of edge computing. To integrate the edge computing platform with the API gateway, we implement a microservice security agent based on the open-source Kong in the EdgeX Foundry framework. Also to provide an easy-to-use approach with Kong, we implement REST APIs for generating new consumers, registering services, configuring access controls. Finally, the usability of the proposed approach is demonstrated by evaluating the round trip time (RTT). The results demonstrate the efficiency of the system and its suitability for real-world applications.

scholarly journals Modeling and Simulation of Cloud Computing Solution for Distributed Space Data Storage and Access In Mobile Communication Networks / Mākoņskaitļošanas Risinājuma Modelēšana Un Simulācija Izkliedētai Kosmiskas Izcelsmes Datu Glabāšanai Un Piekļuvei Mobilos Komunikāciju Tīklos

2013 ◽  
Vol 50 (5) ◽  
pp. 20-28
Author(s):  
M. Kurmis ◽  
D. Dzemydiene ◽  
R. Didziokas ◽  
J. Trokss
Keyword(s):  
Cloud Computing ◽  
Modeling And Simulation ◽  
Communication Networks ◽  
Data Storage ◽  
Mobile Communication ◽  
Storage And Retrieval ◽  
Computing Model ◽  
Computing Paradigm ◽  
Mobile Communication Networks ◽  
Space Data

Abstract Storage and retrieval of space signals require an advanced set of core technologies that can be implemented with a cloud computing paradigm. In this work we propose a cloud computing solution for the distributed space data storage and access in mobile communication networks. The modeling and simulation results show that the proposed solution performs satisfactorily in the space data processing. In the future, experimental verification of the cloud computing model and its implementation are envisaged.

scholarly journals Research on Blockchain-Based Power Data Storage Scheme

2021 ◽  
Vol 292 ◽  
pp. 02007
Author(s):  
Peiguang Chen ◽  
Zihao Tian ◽  
Dong Wang ◽  
Yeyang Zhu
Keyword(s):  
Data Storage ◽  
Power Industry ◽  
Energy Industry ◽  
Computing Paradigm ◽  
Blockchain Technology ◽  
National Economic ◽  
Storage Scheme ◽  
Storage Methods ◽  
National Economic Development ◽  
Industry Sectors

Blockchain technology was invented for bitcoin. It serves as a new computing paradigm with a decentralized framework. For its characteristics such as decentralization, tamper-proofing, and traceability, this technology has been widely used in various industry sectors. Currently, the power industry, as China's basic energy industry, is closely linked to national economic development. In the power industry, power data storage and dispatching are of paramount importance. However, there are some security problems with traditional storage methods. Given this, this paper proposed a blockchain-based power data storage scheme, to enhance the security of power data storage.

scholarly journals Biocompatible artificial synapses based on a zein active layer obtained from maize for neuromorphic computing

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Youngjin Kim ◽  
Chul Hyeon Park ◽  
Jun Seop An ◽  
Seung-Hye Choi ◽  
Tae Whan Kim
Keyword(s):  
Data Processing ◽  
Data Storage ◽  
Active Layer ◽  
Recognition Accuracy ◽  
Implantable Devices ◽  
Digital Data ◽  
Neuromorphic Computing ◽  
Artificial Synapse ◽  
Inference Test ◽  
Digital Data Storage

AbstractArtificial synaptic devices based on natural organic materials are becoming the most desirable for extending their fields of applications to include wearable and implantable devices due to their biocompatibility, flexibility, lightweight, and scalability. Herein, we proposed a zein material, extracted from natural maize, as an active layer in an artificial synapse. The synaptic device exhibited notable digital-data storage and analog data processing capabilities. Remarkably, the zein-based synaptic device achieved recognition accuracy of up to 87% and exhibited clear digit-classification results on the learning and inference test. Moreover, the recognition accuracy of the zein-based artificial synapse was maintained within a difference of less than 2%, regardless of mechanically stressed conditions. We believe that this work will be an important asset toward the realization of wearable and implantable devices utilizing artificial synapses.

scholarly journals Genetic Algorithm for Relational Database Optimization in Reducing Query Execution Time

2018 ◽  
Vol 5 (1) ◽  
pp. 27 ◽  
Author(s):  
Kukuh Triyuliarno Hidayat ◽  
Riza Arifudin ◽  
Alamsyah Alamsyah
Keyword(s):  
Genetic Algorithm ◽  
Data Storage ◽  
Relational Database ◽  
Execution Time ◽  
Data Retrieval ◽  
Data Conversion ◽  
Query Execution ◽  
Algorithm Optimization ◽  
Fitness Value ◽  
Best Execution

The relational database is defined as the database by connecting between tables. Each table has a collection of information. The information is processed in the database by using queries, such as data retrieval, data storage, and data conversion. If the information in the table or data has a large size, then the query process to process the database becomes slow. In this paper, Genetic Algorithm is used to process queries in order to optimize and reduce query execution time. The results obtained are query execution with genetic algorithm optimization to show the best execution time. The genetic algorithm processes the query by changing the structure of the relation and rearranging it. The fitness value generated from the genetic algorithm becomes the best solution. The fitness used is the highest fitness of each experiment results. In this experiment, the database used is  MySQL sample database which is named as employees. The database has a total of over 3,000,000 rows in 6 tables. Queries are designed by using 5 relations in the form of a left deep tree. The execution time of the query is 8.14247 seconds and the execution time after the optimization of the genetic algorithm is 6.08535 seconds with the fitness value of 0.90509. The time generated after optimization of the genetic algorithm is reduced by 25.3%. It shows that genetic algorithm can reduce query execution time by optimizing query in the part of relation. Therefore, query optimization with genetic algorithm can be an alternative solution and can be used to maximize query performance.

scholarly journals Graphene Oxide: Graphene Quantum Dot Nanocomposite for Better Memristic Switching Behaviors

Nanomaterials ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 1448
Author(s):  
Lei Li
Keyword(s):  
Graphene Oxide ◽  
Data Storage ◽  
Indium Tin Oxide ◽  
Nonvolatile Memory ◽  
Conduction Mechanism ◽  
Graphene Quantum Dots ◽  
Charge Trapping ◽  
Reset Voltage ◽  
Ultrahigh Density ◽  
A Charge

Tristable memristic switching provides the capability for multi-bit data storage. In this study, all-inorganic multi-bit memory devices were successfully manufactured by the attachment of graphene quantum dots (GQDs) onto graphene oxide (GO) through a solution-processable method. By means of doping GQDs as charge-trapping centers, the device indium-tin oxide (ITO)/GO:0.5 wt%GQDs/Ni revealed controllable memristic switching behaviors that were tunable from binary to ternary, and remarkably enhanced in contrast with ITO/GO/Ni. It was found that the device has an excellent performance in memristic switching parameters, with a SET1, SET2 and RESET voltage of −0.9 V, −1.7 V and 5.15 V, as well as a high ON2/ON1/OFF current ratio (103:102:1), and a long retention time (104 s) together with 100 successive cycles. The conduction mechanism of the binary and ternary GO-based memory cells was discussed in terms of experimental data employing a charge trapping-detrapping mechanism. The reinforcement effect of GQDs on the memristic switching of GO through cycle-to-cycle operation has been extensively investigated, offering great potential application for multi-bit data storage in ultrahigh-density, nonvolatile memory.

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