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Статті в журналах з теми "Memory Devices - Classification"
Bezerra, Vitor Hugo, Victor Guilherme Turrisi da Costa, Sylvio Barbon Junior, Rodrigo Sanches Miani, and Bruno Bogaz Zarpelão. "IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices." Sensors 19, no. 14 (July 19, 2019): 3188. http://dx.doi.org/10.3390/s19143188.
Повний текст джерелаHwang, Yeongjin, Jeong Hoon Jeon, Juhyun Lee, Jonghyuk Yoon, Felix Sunjoo Kim, and Hyungjin Kim. "Effect of Threshold Voltage Window and Variation of Organic Synaptic Transistor for Neuromorphic System." Journal of Nanoscience and Nanotechnology 21, no. 8 (August 1, 2021): 4303–9. http://dx.doi.org/10.1166/jnn.2021.19393.
Повний текст джерелаK I, Ravikumar. "Memristor-Based Deep Learning Classification Model for Object Detection." ECS Transactions 107, no. 1 (April 24, 2022): 277–85. http://dx.doi.org/10.1149/10701.0277ecst.
Повний текст джерелаPérez Arteaga, Sandra, Ana Lucila Sandoval Orozco, and Luis Javier García Villalba. "Analysis of Machine Learning Techniques for Information Classification in Mobile Applications." Applied Sciences 13, no. 9 (April 27, 2023): 5438. http://dx.doi.org/10.3390/app13095438.
Повний текст джерелаXu, Peng, Zhihua Xiao, Xianglong Wang, Lei Chen, Chao Wang, and Fengwei An. "A Multi-Core Object Detection Coprocessor for Multi-Scale/Type Classification Applicable to IoT Devices." Sensors 20, no. 21 (October 31, 2020): 6239. http://dx.doi.org/10.3390/s20216239.
Повний текст джерелаYauri, Ricardo, and Rafael Espino. "Edge device for movement pattern classification using neural network algorithms." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 1 (April 1, 2023): 229. http://dx.doi.org/10.11591/ijeecs.v30.i1.pp229-236.
Повний текст джерелаSingh Yadav, Ram, Aniket Sadashiva, Amod Holla, Pranaba Kishor Muduli, and Debanjan Bhowmik. "Impact of edge defects on the synaptic characteristic of a ferromagnetic domain-wall device and on on-chip learning." Neuromorphic Computing and Engineering 3, no. 3 (August 25, 2023): 034006. http://dx.doi.org/10.1088/2634-4386/acf0e4.
Повний текст джерелаLee, Hyungkeuk, NamKyung Lee, and Sungjin Lee. "A Method of Deep Learning Model Optimization for Image Classification on Edge Device." Sensors 22, no. 19 (September 27, 2022): 7344. http://dx.doi.org/10.3390/s22197344.
Повний текст джерелаKwon, Dongseok, Hyeongsu Kim, Kyu-Ho Lee, Joon Hwang, Wonjun Shin, Jong-Ho Bae, Sung Yun Woo, and Jong-Ho Lee. "Super-steep synapses based on positive feedback devices for reliable binary neural networks." Applied Physics Letters 122, no. 10 (March 6, 2023): 102101. http://dx.doi.org/10.1063/5.0131235.
Повний текст джерелаQian, Xuwei, Renlong Hang, and Qingshan Liu. "ReX: An Efficient Approach to Reducing Memory Cost in Image Classification." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (June 28, 2022): 2099–107. http://dx.doi.org/10.1609/aaai.v36i2.20106.
Повний текст джерелаДисертації з теми "Memory Devices - Classification"
Henke, M., and G. Gerlach. "A multi-layered variable stiffness device based on smart form closure actuators." Sage, 2016. https://tud.qucosa.de/id/qucosa%3A35622.
Повний текст джерелаMondal, Sandip. "Fully Solution Processed Flash Memory." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4131.
Повний текст джерелаZheng, Yichu. "pinMOS Memory: A novel, diode-based organic memory device." 2019. https://tud.qucosa.de/id/qucosa%3A72161.
Повний текст джерелаEs wird ein neuartiges, organisches kapazitives Speicherelement demonstriert, das p-i-n-Metalloxid-Halbleiter (pinMOS) Speicher genannt wird und eine Mehrfachbitspeicherung besitzt, die elektrisch und optisch programmiert und ausgelesen werden kann. Die auf einer Diode basierende Architektur vereinfacht den Herstellungsprozess sowie die weitere Optimierung und könnte sogar Inspiration für neue kapazitive Speichermedien sein. Darüber hinaus basiert dieses innovative pinMOS Speicherelement auf der lokalen Aufladung einer integrierten Kapazität und nicht auf einem zusätzlichem “Floating Gate”. Bevor das Speicherelement wie gewünscht funktioniert, muss der Leckstrom, der durch die laterale Aufladung der dotierten Schichten außerhalb des aktiven Bereichs verursacht wird, unterdrückt werden. Deshalb werden in dieser Arbeit zuerst die lateralen Aufladungseffekte in organischen Leuchtdioden (OLEDs) untersucht. Beim Vergleich verschiedener Device-Strukturen wird die Existenz von lateralen Stromflüssen im Zentimeterbereich in den n- und p-dotierten Schichten gezeigt, was zu einer unerwünschten erhöhten Kapazität und folglich einem höheren Leckstrom führt. Diese laterale Aufladung kann durch die Strukturierung der dotierten Schichten kontrolliert werden, was zu extrem geringen Gleichgewichtsleckströmen in den OLEDs (10-7 mA/cm2 bei -1 V) resultiert. Es wird auch gezeigt, dass die lateralen Ströme genutzt werden können um die spezifische Leitfähigkeit sowie die Aktivierungsenergie der einzelnen dotierten Schichten zu extrahieren, wenn diese mit einem RC-Modell modelliert werden. Im zweiten Teil werden pinMOS Speicherelemente, die auf der Diode mit strukturierten dotierten Schichten basieren, untersucht. Das Speicherverhalten, dass durch Kapazitätsschaltung für elektrische Signale und als Lichtemission für optische Signale gezeigt wird, kann entweder durch die angelegte Spannung, beziehungsweise durch die Belichtung mit ultraviolettem Licht eingestellt werden. Die Wirkungsweise wird durch die Existenz quasistatischer Gleichgewichte sowie durch die Größenänderung der Raumladungszonen erklärt. Der pinMOS Speicher zeigt eine hervorragende Wiederholbarkeit, eine Beständigkeit über mehr als 104 Schreiben-Lesen-Löschen-Lesen Zyklen und aktuell schon eine Retentionszeit von über 24 h. Weiterhin offenbaren erste Versuche in der Nachahmung von Neuronaler Plastizität das Potenzial von pinMOS Speichern für Anwendungen im “Neuromorphic Computing”. Insgesamt deuten die Ergebnisse an, dass pinMOS Speicher prinzipiell vielversprechend für eine Vielzahl von zukünftigen Anwendungen in elektronischen und photonischen Schaltkreisen ist. Ein tiefgreifendes Verständnis von diesem Konzept neuartiger Speicherelemente, für das diese Arbeit eine wichtige Grundlage bildet, ist notwendig, um weitere Verbesserungen zu entwickeln.:1 Introduction 1 2 Fundamentals of organic semiconductors 5 2.1 Electronic states of a molecule 5 2.1.1 Atomic orbitals and molecular orbitals 5 2.1.2 Solid states 9 2.1.3 Singlet and triplet states 12 2.2 Charge transport 13 2.2.1 Charge carrier mobility 13 2.2.2 Charge carrier transport 14 2.3 Charge injection 17 2.3.1 Current limitation 17 2.3.2 Charge injection mechanisms 20 2.4 Doping 22 3 Organic junctions and devices 25 3.1 Metal-semiconductor junction 25 3.1.1 Schottky junction 25 3.1.2 Surface states 27 3.2 Metal-oxide-semiconductor capacitor 29 3.3 Junctions and diodes 31 3.3.1 PN junction and diode 31 3.3.2 PIN junction and diode 32 4 Organic non-volatile memory devices 35 4.1 Basic concepts 35 4.2 Organic resistive memory devices 37 4.2.1 Device architecture and switching behavior 38 4.2.2 Working mechanisms 38 4.3 Organic transistor-based memory devices 41 4.3.1 Organic field-effect transistor and memory devices based thereon 41 4.3.2 Floating gate memory 43 4.3.3 Charge trapping memory 45 4.4 Organic ferroelectric memory devices 46 4.4.1 Ferroelectric capacitor memory 47 4.4.2 Ferroelectric transistor memory 48 4.4.3 Ferroelectric diode memory 49 5 Experimental methods 53 5.1 Device fabrication 53 5.2 Device characterization 55 5.3 Materials 57 6 Lateral current flow in semiconductor devices having crossbar electrodes 61 6.1 Introduction 61 6.2 Device architecture 62 6.3 Characteristics comparison between unstructured and structured devices 63 6.3.1 Charging measurement 63 6.3.2 Current-voltage characteristics 64 6.3.3 Capacitance-frequency characteristics 67 6.4 Influence of conductivity of doped layers 69 6.4.1 Dependence on doped layers thickness 69 6.4.2 Dependence on temperature 73 6.5 Lateral charging simulation 74 6.5.1 Analytical description 74 6.5.2 RC circuit simulation 76 6.5.3 Parameters for doped layers gained by simulation 79 6.6 Pseudo trap analysis 81 6.6.1 The pseudo trap density of states determination 81 6.6.2 The pseudo trap analysis under simulated identical conditions 84 6.7 Summary 85 7 The pinMOS memory: novel diode-capacitor memory with multiple-bit storage 87 7.1 Introduction 87 7.2 Device architecture 88 7.2.1 Dependence on layout and pixel 89 7.2.2 Fundamental memory behavior characterization 93 7.3 Working mechanism 96 7.3.1 Working mechanism of quasi-steady states 97 7.3.2 Working mechanism of dynamic states 101 7.4 Tunability of the memory effect 105 7.4.1 Operation parameters 106 7.4.2 Photoinduced tunability 108 7.4.3 Intrinsic layer thickness 110 7.5 Potential in neuromorphic computing application 111 7.5.1 Extracting capacitance at 0 V sequentially 112 7.5.2 Mimicking the long-term plasticity (LTP) behavior 113 7.6 Summary 114 8 Optoelectronic properties of pinMOS memory 117 8.1 Introduction 117 8.2 Measurement setup 117 8.3 pinMOS memory emission intensity 118 8.4 Pulse characteristics and device brightness 119 8.5 Conclusion 124 9 Conclusion 125 Bibliography 129 List of Figures 145 List of Tables 151 List of Abbreviations 153 Publications and Conference 157 Acknowledgment 159
(6838184), Parami Wijesinghe. "Neuro-inspired computing enhanced by scalable algorithms and physics of emerging nanoscale resistive devices." 2019.
Знайти повний текст джерелаDeep ‘Analog Artificial Neural Networks’ (AANNs) perform complex classification problems with high accuracy. However, they rely on humongous amount of power to perform the calculations, veiling the accuracy benefits. The biological brain on the other hand is significantly more powerful than such networks and consumes orders of magnitude less power, indicating some conceptual mismatch. Given that the biological neurons are locally connected, communicate using energy efficient trains of spikes, and the behavior is non-deterministic, incorporating these effects in Artificial Neural Networks (ANNs) may drive us few steps towards a more realistic neural networks.
Emerging devices can offer a plethora of benefits including power efficiency, faster operation, low area in a vast array of applications. For example, memristors and Magnetic Tunnel Junctions (MTJs) are suitable for high density, non-volatile Random Access Memories when compared with CMOS implementations. In this work, we analyze the possibility of harnessing the characteristics of such emerging devices, to achieve neuro-inspired solutions to intricate problems.
We propose how the inherent stochasticity of nano-scale resistive devices can be utilized to realize the functionality of spiking neurons and synapses that can be incorporated in deep stochastic Spiking Neural Networks (SNN) for image classification problems. While ANNs mainly dwell in the aforementioned classification problem solving domain, they can be adapted for a variety of other applications. One such neuro-inspired solution is the Cellular Neural Network (CNN) based Boolean satisfiability solver. Boolean satisfiability (k-SAT) is an NP-complete (k≥3) problem that constitute one of the hardest classes of constraint satisfaction problems. We provide a proof of concept hardware based analog k-SAT solver that is built using MTJs. The inherent physics of MTJs, enhanced by device level modifications, is harnessed here to emulate the intricate dynamics of an analog, CNN based, satisfiability (SAT) solver.
Furthermore, in the effort of reaching human level performance in terms of accuracy, increasing the complexity and size of ANNs is crucial. Efficient algorithms for evaluating neural network performance is of significant importance to improve the scalability of networks, in addition to designing hardware accelerators. We propose a scalable approach for evaluating Liquid State Machines: a bio-inspired computing model where the inputs are sparsely connected to a randomly interlinked reservoir (or liquid). It has been shown that biological neurons are more likely to be connected to other neurons in the close proximity, and tend to be disconnected as the neurons are spatially far apart. Inspired by this, we propose a group of locally connected neuron reservoirs, or an ensemble of liquids approach, for LSMs. We analyze how the segmentation of a single large liquid to create an ensemble of multiple smaller liquids affects the latency and accuracy of an LSM. In our analysis, we quantify the ability of the proposed ensemble approach to provide an improved representation of the input using the Separation Property (SP) and Approximation Property (AP). Our results illustrate that the ensemble approach enhances class discrimination (quantified as the ratio between the SP and AP), leading to improved accuracy in speech and image recognition tasks, when compared to a single large liquid. Furthermore, we obtain performance benefits in terms of improved inference time and reduced memory requirements, due to lower number of connections and the freedom to parallelize the liquid evaluation process.
Частини книг з теми "Memory Devices - Classification"
Disabato, Simone. "Deep and Wide Tiny Machine Learning." In Special Topics in Information Technology, 79–92. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15374-7_7.
Повний текст джерелаShahin, Mohammad, FFrank Chen, Hamed Bouzary, Ali Hosseinzadeh, and Rasoul Rashidifar. "Implementation of a Novel Fully Convolutional Network Approach to Detect and Classify Cyber-Attacks on IoT Devices in Smart Manufacturing Systems." In Lecture Notes in Mechanical Engineering, 107–14. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18326-3_11.
Повний текст джерелаVerma, R. "Applications of Hard Ferrites in Memory Devices." In Materials Research Foundations, 185–206. Materials Research Forum LLC, 2023. http://dx.doi.org/10.21741/9781644902318-7.
Повний текст джерелаTolia, Dhanesh, Sayaboina Jagadeeshwar, Jayendra Kumar, Pratul Arvind, and Arvind R. Yadav. "Image Processing on Resource-Constrained Devices." In Futuristic Projects in Energy and Automation Sectors: A Brief Review of New Technologies Driving Sustainable Development, 273–92. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815080537123010017.
Повний текст джерелаPisani, Flávia, Fabíola M. C. de Oliveira, Eduardo S. Gama, Roger Immich, Luiz F. Bittencourt, and Edson Borin. "Fog Computing on Constrained Devices: Paving the Way for the Future IoT." In Advances in Edge Computing: Massive Parallel Processing and Applications. IOS Press, 2020. http://dx.doi.org/10.3233/apc200003.
Повний текст джерелаТези доповідей конференцій з теми "Memory Devices - Classification"
Chang Chen, Liangwei Cai, Yang Xiang, and Jun Li. "SwinTop: Optimizing memory efficiency of packet classification in network devices." In 2015 IEEE International Conference on Communication Software and Networks (ICCSN). IEEE, 2015. http://dx.doi.org/10.1109/iccsn.2015.7296139.
Повний текст джерелаGan, L. R., and C. Wang. "Modeling of Embedded Split-Gate Flash Memory for Pattern Classification." In 2019 International Conference on Solid State Devices and Materials. The Japan Society of Applied Physics, 2019. http://dx.doi.org/10.7567/ssdm.2019.ps-10-15.
Повний текст джерелаLi, Xianfeng, and Yanhua Shao. "Memory compression for Recursive Flow Classification Algorithm in Network Packet Processing Devices." In 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2018. http://dx.doi.org/10.1109/iaeac.2018.8577888.
Повний текст джерелаIsuyama, Vivian Kimie, and Bruno De Carvalho Albertini. "Comparison of Convolutional Neural Network Models for Mobile Devices." In Workshop em Desempenho de Sistemas Computacionais e de Comunicação. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/wperformance.2021.15724.
Повний текст джерелаEichmann, George, A. Kostrzewski, and Y. Li. "Optical arithmetic computing using a linear associative memory." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/oam.1987.thn3.
Повний текст джерелаAdegbija, Tosiron, Anita Rogacs, Chandrakant Patel, and Ann Gordon-Ross. "Enabling Right-Provisioned Microprocessor Architectures for the Internet of Things." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-50173.
Повний текст джерелаSegal, Julie, Arman Sagatelian, Bob Hodgkins, Tom Ho, Ben Chu, Tony Singh, and Harvey Berman. "In-Line Defect to Bitmap Signature Correlation: A Shortcut to Physical FA Results." In ISTFA 2000. ASM International, 2000. http://dx.doi.org/10.31399/asm.cp.istfa2000p0081.
Повний текст джерелаCosta, Victor G. Turrisi da, Bruno Bogaz Zarpelão, Rodrigo Sanches Miani, and Sylvio Barbon Junior. "Online detection of Botnets on Network Flows using Stream Mining." In Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/sbrc.2018.2418.
Повний текст джерелаKyuma, K., and J. Ohta. "Optical neurodevices using a smart detector array." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.thdd2.
Повний текст джерелаUngureanu, Florina, Tiberius Dumitriu, Vasile ion Manta, and Corina Cimpanu. "COGNITIVE LOAD AND SHORT TERM MEMORY EVALUATION BASED ON EEG TECHNIQUES." In eLSE 2017. Carol I National Defence University Publishing House, 2017. http://dx.doi.org/10.12753/2066-026x-17-116.
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