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Статті в журналах з теми "Neuromorphic applications"
Mikki, Said. "Generalized Neuromorphism and Artificial Intelligence: Dynamics in Memory Space." Symmetry 16, no. 4 (April 18, 2024): 492. http://dx.doi.org/10.3390/sym16040492.
Повний текст джерелаPark, Jisoo, Jihyun Shin, and Hocheon Yoo. "Heterostructure-Based Optoelectronic Neuromorphic Devices." Electronics 13, no. 6 (March 14, 2024): 1076. http://dx.doi.org/10.3390/electronics13061076.
Повний текст джерелаHenkel, Jorg. "Stochastic Computing for Neuromorphic Applications." IEEE Design & Test 38, no. 6 (December 2021): 4. http://dx.doi.org/10.1109/mdat.2021.3126288.
Повний текст джерелаDiao, Yu, Yaoxuan Zhang, Yanran Li, and Jie Jiang. "Metal-Oxide Heterojunction: From Material Process to Neuromorphic Applications." Sensors 23, no. 24 (December 12, 2023): 9779. http://dx.doi.org/10.3390/s23249779.
Повний текст джерелаSchuman, Catherine, Robert Patton, Shruti Kulkarni, Maryam Parsa, Christopher Stahl, N. Quentin Haas, J. Parker Mitchell, et al. "Evolutionary vs imitation learning for neuromorphic control at the edge*." Neuromorphic Computing and Engineering 2, no. 1 (January 24, 2022): 014002. http://dx.doi.org/10.1088/2634-4386/ac45e7.
Повний текст джерелаKurshan, Eren, Hai Li, Mingoo Seok, and Yuan Xie. "A Case for 3D Integrated System Design for Neuromorphic Computing and AI Applications." International Journal of Semantic Computing 14, no. 04 (December 2020): 457–75. http://dx.doi.org/10.1142/s1793351x20500063.
Повний текст джерелаHuang, Heyi, Chen Ge, Zhuohui Liu, Hai Zhong, Erjia Guo, Meng He, Can Wang, Guozhen Yang, and Kuijuan Jin. "Electrolyte-gated transistors for neuromorphic applications." Journal of Semiconductors 42, no. 1 (January 1, 2021): 013103. http://dx.doi.org/10.1088/1674-4926/42/1/013103.
Повний текст джерелаPalmer, Chris. "Neuromorphic Computing Advances Deep-Learning Applications." Engineering 6, no. 8 (August 2020): 854–56. http://dx.doi.org/10.1016/j.eng.2020.06.010.
Повний текст джерелаLv, Wenxing, Jialin Cai, Huayao Tu, Like Zhang, Rongxin Li, Zhe Yuan, Giovanni Finocchio, et al. "Stochastic artificial synapses based on nanoscale magnetic tunnel junction for neuromorphic applications." Applied Physics Letters 121, no. 23 (December 5, 2022): 232406. http://dx.doi.org/10.1063/5.0126392.
Повний текст джерелаWang, Ye-Guo. "Applications of Memristors in Neural Networks and Neuromorphic Computing: A Review." International Journal of Machine Learning and Computing 11, no. 5 (September 2021): 350–56. http://dx.doi.org/10.18178/ijmlc.2021.11.5.1060.
Повний текст джерелаДисертації з теми "Neuromorphic applications"
Chen, Xing. "Modeling and simulations of skyrmionic neuromorphic applications." Thesis, université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST083.
Повний текст джерелаSpintronics nanodevices, which exploit both the magnetic and electrical properties of electrons, have emerged to bring various exciting characteristics promising for neuromorphic computing. Magnetic textures, such as domain walls and skyrmions, are particularly intriguing as neuromorphic components because they can support different functionalities due to their rich physical mechanisms. How the skyrmion dynamics can be utilized to build energy efficient neuromorphic hardware, and how deep learning can help achieve fast and accurate tests and validations of the proposals form the central topics of this thesis. The major contributions and innovations of this thesis can be summarized as follows: 1. Numerical and theoretical studies on skyrmion dynamics in confined nanostructures. We explore the skyrmion dynamics in terms of size, velocity, energy, and stability in a width-varying nanotrack. We found nanoscale skyrmion with small sizes could be obtained by employing this asymmetric structure. We also obtain a tradeoff between the nanotrack width (storage density) and the skyrmion motion velocity (data access speed). We study the skyrmion dynamics under voltage excitation through the voltage-controlled magnetic anisotropy effect in a circular thin film. We find that the breathing skyrmion can be analogized as a modulator. These findings could help us design efficient neuromorphic devices. 2. Skyrmion based device applications for neuromorphic computing. We present a compact Leaky-Integrate-Fire spiking neuron device by exploiting the current-driven skyrmion dynamics in a wedge-shaped nanotrack. We propose a True random number generators based on continuous skyrmion thermal Brownian motion in a confined geometry at room temperature. Our design are promising in emerging low power neuromorphic computing system, such as spiking neural network and stochastic/ probabilistic computing neuron network.3. A data-driven approach for modeling dynamical physical systems based on the Neural Ordinary Differential Equations (ODEs). We show that the adapted formalisms of Neural ODEs, designed for spintronics, can accurately predict the behavior of a non-ideal nanodevice, including noise, after training on a minimal set of micromagnetic simulations or experimental data, with new inputs and material parameters not belonging to the training data. With this modeling strategy, we can perform more complicated computational tasks, such as Mackey-Glass time-series predictions and spoken digit recognition, using the trained models of spintronic systems, with high accuracy and fast speed compared to conventional micromagnetic simulations
Shi, Yuanyuan. "Two dimensional materials based electronic synapses for neuromorphic applications." Doctoral thesis, Universitat de Barcelona, 2018. http://hdl.handle.net/10803/663415.
Повний текст джерелаEl cerebro humano puede realizar de forma sencilla infinidad de operaciones que los ordenadores no pueden hacer, pueden aprender naturalmente adaptando su estructura física, y consumen mucho menos energía. La razón es que el cerebro humano usa una sofisticada y muy densa red neuronal que procesa y almacena la información en paralelo. Este masivo paralelismo es la genuina característica que los ordenadores no pueden igualar, ya que éstos procesan y almacenan la información en unidades distintas, creando un embudo que limita sus prestaciones. Por lo tanto, emular el funcionamiento del cerebro utilizando componentes electrónicos es extremadamente importante, y se ha convertido en la obsesión de las mayores empresas. Las primeras redes neuronales artificiales para el desarrollo de inteligencia artificial están basadas en transistores, ya que éstos han sido la base de todos los dispositivos electrónicos modernos. Sin embargo, estudios recientes indican que los memristores podrían ser más idóneos para emular la interacción entre neuronas. En concreto, dos neuronas interactúan entre ellas a través de sinapsis, es decir, finas membranas que cambian su resistividad dependiendo de los impulsos eléctricos emitidos por las dos neuronas. La estructura y principio de funcionamiento de una sinapsis es muy similar al de un memristor, el cual presenta la ventaja de tener una estructura más simple y un coste de fabricación más bajo que un transistor. En esta tesis doctoral hemos desarrollado memristores avanzados utilizando materiales bidimensionales, como el grafeno y, especialmente, el nitruto de boto hexagonal con estructura multicapa. Nuestros experimentos y simulaciones indican que los dispositivos metal/h-BN/metal pueden ser utilizados como sinapsis electrónicas, ya que muestran comportamientos sinápticos en un único dispositivo. En nuestros dispositivos hemos observado short term plasticity, long term plasticity, spike timing dependent plasticity, y synapse relaxation. El régimen de funcionamiento puede ser controlado modificando la amplitud, duración e intervalo entre los pulsos aplicados. Además, las sinapsis electrónicas hechas mediante estructuras metal/h-BN/metal muestran un proceso de relajación muy repetitivo y con una baja variabilidad nunca observada anteriormente. Además, el consumo de potencia es muy bajo tanto en reposo (0.1 fW) como en modo volátil (600 pW).
Uppala, Roshni. "Simulating Large Scale Memristor Based Crossbar for Neuromorphic Applications." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429296073.
Повний текст джерелаLai, Qianxi. "Electrically configurable materials and devices for intelligent neuromorphic applications." Diss., Restricted to subscribing institutions, 2009. http://proquest.umi.com/pqdweb?did=1872061101&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.
Повний текст джерелаMandal, Saptarshi. "Study of Mn doped HfO2 based Synaptic Devices for Neuromorphic Applications." University of Toledo / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1384535471.
Повний текст джерелаPedró, Puig Marta. "Implementation of unsupervised learning mechanisms on OxRAM devices for neuromorphic computing applications." Doctoral thesis, Universitat Autònoma de Barcelona, 2019. http://hdl.handle.net/10803/667894.
Повний текст джерелаThe present thesis compiles the results of the research oriented to provide a methodology for the electrical characterization, modeling and simulation of resistive switching devices, taking into consideration neuromorphic applications based on unsupervised learning This is widely demanded today as a low-consumption solution to the following issues: on the one hand, the speed limitations that take place in data transfer between the memory and processing units that takes place in conventional computer architectures. On the other hand, the growing need for low-power computational systems that perform tasks of classification, analysis and inference of massive amounts of data (for example, for Big Data applications), together with pattern recognition, prediction of behaviors and decision-making tasks (for applications focused on Internet-of-Things, among others). Specifically, Oxide-based Resistive Random Access Memory (OxRAM) devices are investigated as candidates for the electronic implementation of synapses in physical artificial neural networks, also referred to as neuromorphic architectures. First of all, a theoretical introduction to the different electronic technologies with resistive switching and non-volatile memory properties is provided. The figures of merit demonstrated and projected of each one of them are indicated according to the International Roadmap for Devices and Systems of 2018. With this first chapter, the intention is to provide the reader with the necessary background required to understand the results outlined in the following chapters. Next, and by using a bottom-up approach divided into the three following chapters, the procedures and results of the electrical characterization and modeling of the OxRAM devices studied for the implementation of analog electronic synapses are discussed. As a starting point, it is experimentally verified that the devices meet the requirements for the indicated application. In the following chapter, two fundamental learning rules are demonstrated experimentally in order to permit the execution of an autonomous (unsupervised) learning algorithm on a neuromorphic architecture based on the tested devices. The proven learning rules allow the devices to emulate certain processes and learning mechanisms reported in the neuroscience field, such as spike-timing dependent plasticity, or the classical conditioning phenomenon, for which Pavlov’s dog experiment is replicated as to establish the foundations of associative learning, to be implemented between two or more synaptic devices. To conclude this part related to analog electronic synapses, the hardware adaptation of an unsupervised learning algorithm is proposed. The designed algorithm provides the system with the property of self-organization, in such a way that, once trained, the physical neuronal network shows a topographical organization in its output layer, which is characteristic of the sensory processing areas of the biological brain. Furthermore, the proposed design and algorithm allow the concatenation of several neuronal networks, in order to execute cognitive tasks of a more complex nature, such as the association of different attributes to the same concept, related to hierarchical computation. The last chapter is dedicated to the study of OxRAM devices when a low-power mode is considered, for the implementation of binary synapses. Again using a bottom-up perspective, the chapter begins with the electrical characterization and modeling of the devices, which in this case constitute a neuromorphic chip. A probabilistic learning rule is demonstrated, which is then used in an unsupervised on-line learning algorithm designed for the inference and prediction of periodic temporal sequences. Finally, the differences and similarities between the two algorithms described in the thesis are discussed, and a proposal is made as to how each of these can be used in a joint and complementary way.
Petre, Csaba. "Sim2spice a tool for compiling simulink designs on FPAA and applications to neuromorphic circuits /." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31820.
Повний текст джерелаCommittee Chair: Paul Hasler; Committee Member: Christopher Rozell; Committee Member: David Anderson. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Herrmann, Eric. "A Novel Gate Controlled Metal Oxide Resistive Memory Cell and its Applications." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1540565326482153.
Повний текст джерелаMARRONE, FRANCESCO. "Memristor-based hardware accelerators: from device modeling to AI applications." Doctoral thesis, Politecnico di Torino, 2022. http://hdl.handle.net/11583/2972305.
Повний текст джерелаSECCO, JACOPO. "Memristor Platforms for Pattern Recognition Memristor Theory, Systems and Applications." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2680573.
Повний текст джерелаКниги з теми "Neuromorphic applications"
Kozma, Robert, Robinson E. Pino, and Giovanni E. Pazienza, eds. Advances in Neuromorphic Memristor Science and Applications. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4491-2.
Повний текст джерелаKozma, Robert. Advances in Neuromorphic Memristor Science and Applications. Dordrecht: Springer Netherlands, 2012.
Знайти повний текст джерелаBeaton, Paul Timothy, ed. Frontiers in Memristive Materials for Neuromorphic Processing Applications. Washington, D.C.: National Academies Press, 2020. http://dx.doi.org/10.17226/25938.
Повний текст джерелаC, Merrill Walter, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Division., eds. Neuromorphic learning of continuous-valued mappings from noise-corrupted data: Application to real-time adaptive control. [Washington, DC]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.
Знайти повний текст джерелаBartolozzi, Chiara, Emre O. Neftci, and Elisabetta Chicca, eds. Neuromorphic Engineering Systems and Applications. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88971-723-1.
Повний текст джерелаvan Schaik, André, Tobi Delbruck, and Jennifer Hasler, eds. Neuromorphic Engineering Systems and Applications. Frontiers Media SA, 2015. http://dx.doi.org/10.3389/978-2-88919-454-4.
Повний текст джерелаDong, Yibo, Min Gu, Elena Goi, Yangyundou Wang, and Zhengfen Wan. Neuromorphic Photonic Devices and Applications. Elsevier, 2023.
Знайти повний текст джерелаWang, Jing, Min Gu, Elena Goi, Yangyundou Wang, and Zhengfen Wan. Neuromorphic Photonic Devices and Applications. Elsevier, 2023.
Знайти повний текст джерелаPazienza, Giovanni E., Robert Kozma, and Robinson E. Pino. Advances in Neuromorphic Memristor Science and Applications. Springer Netherlands, 2016.
Знайти повний текст джерелаAdvances In Neuromorphic Memristor Science And Applications. Springer, 2012.
Знайти повний текст джерелаЧастини книг з теми "Neuromorphic applications"
Narduzzi, Simon, Loreto Mateu, Petar Jokic, Erfan Azarkhish, and Andrea Dunbar. "Benchmarking Neuromorphic Computing for Inference." In Industrial Artificial Intelligence Technologies and Applications, 1–19. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003377382-1.
Повний текст джерелаMilo, Valerio, Gerardo Malavena, Christian Monzio Compagnoni, and Daniele Ielmini. "Memristive/CMOS Devices for Neuromorphic Applications." In Springer Handbook of Semiconductor Devices, 1167–99. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-79827-7_32.
Повний текст джерелаLu, Wei. "RRAM Fabric for Neuromorphic Computing Applications." In From Artificial Intelligence to Brain Intelligence, 175–90. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338215-10.
Повний текст джерелаGómez-Vilda, Pedro, José Manuel Ferrández-Vicente, Victoria Rodellar-Biarge, Agustín Álvarez-Marquina, Luis Miguel Mazaira-Fernández, Rafael Martínez-Olalla, and Cristina Muñoz-Mulas. "Neuromorphic Detection of Vowel Representation Spaces." In New Challenges on Bioinspired Applications, 1–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21326-7_1.
Повний текст джерелаHu, Xiaofang, Shukai Duan, Wenbo Song, Jiagui Wu, and Pinaki Mazumder. "Memristor-based Cellular Nonlinear/Neural Network: Design, Analysis and Applications." In Neuromorphic Circuits for Nanoscale Devices, 275–301. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338918-11.
Повний текст джерелаPino, Robinson E. "Computational Intelligence and Neuromorphic Computing Architectures." In Advances in Neuromorphic Memristor Science and Applications, 77–88. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4491-2_6.
Повний текст джерелаIsik, Murat, Hiruna Vishwamith, Yusuf Sur, Kayode Inadagbo, and I. Can Dikmen. "NEUROSEC: FPGA-Based Neuromorphic Audio Security." In Applied Reconfigurable Computing. Architectures, Tools, and Applications, 134–47. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55673-9_10.
Повний текст джерелаRyan, Kevin, Sansiri Tanachutiwat, and Wei Wang. "3D CMOL Crossnet for Neuromorphic Network Applications." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 1–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02427-6_1.
Повний текст джерелаDias, C., J. Ventura, and P. Aguiar. "Memristive-Based Neuromorphic Applications and Associative Memories." In Advances in Memristors, Memristive Devices and Systems, 305–42. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51724-7_13.
Повний текст джерелаMaji, Prasenjit, Ramapati Patra, Kunal Dhibar, and Hemanta Kumar Mondal. "SNN Based Neuromorphic Computing Towards Healthcare Applications." In Internet of Things. Advances in Information and Communication Technology, 261–71. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45878-1_18.
Повний текст джерелаТези доповідей конференцій з теми "Neuromorphic applications"
Shastri, Bhavin J., Alexander N. Tait, Mitchell A. Nahmias, Thomas Ferreira de Lima, Hsuan-Tung Peng, and Paul R. Prucnal. "Neuromorphic Photonic Processor Applications." In 2019 IEEE Photonics Society Summer Topical Meeting Series (SUM). IEEE, 2019. http://dx.doi.org/10.1109/phosst.2019.8795013.
Повний текст джерелаPrucnal, Paul R., Alexander N. Tait, Mitchell A. Nahmias, Thomas Ferreira de Lima, Hsuan-Tung Peng, and Bhavin J. Shastri. "Multiwavelength Neuromorphic Photonics." In CLEO: Applications and Technology. Washington, D.C.: OSA, 2019. http://dx.doi.org/10.1364/cleo_at.2019.jm3m.3.
Повний текст джерелаBrückerhoff-Plückelmann, Frank, Johannes Feldmann, Helge Gehring, Wen Zhou, C. David Wright, Harish Bhaskaran, and Wolfram Pernice. "Ultra-low Crosstalk Multiplexer for Neuromorphic Photonic Data Processing." In CLEO: Applications and Technology. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/cleo_at.2022.jth3a.51.
Повний текст джерелаAimone, James B., Ojas Parekh, and William Severa. "Neural computing for scientific computing applications." In NCS '17: Neuromorphic Computing Symposium. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3183584.3183618.
Повний текст джерелаPatton, Robert, Prasanna Date, Shruti Kulkarni, Chathika Gunaratne, Seung-Hwan Lim, Guojing Cong, Steven R. Young, Mark Coletti, Thomas E. Potok, and Catherine D. Schuman. "Neuromorphic Computing for Scientific Applications." In 2022 IEEE/ACM Redefining Scalability for Diversely Heterogeneous Architectures Workshop (RSDHA). IEEE, 2022. http://dx.doi.org/10.1109/rsdha56811.2022.00008.
Повний текст джерелаCardwell, Suma G., and Frances S. Chance. "Dendritic Computation for Neuromorphic Applications." In ICONS '23: 2023 International Conference on Neuromorphic Systems. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3589737.3606001.
Повний текст джерелаBuckley, S. M., J. Chiles, A. N. McCaughan, R. P. Mirin, S. W. Nam, and J. M. Shainline. "Light sources for neuromorphic computing." In CLEO: Applications and Technology. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/cleo_at.2018.jw2a.29.
Повний текст джерелаPrucnal, Paul R., and Thomas Ferreira de Lima. "Neuromorphic photonics for real-time applications." In Emerging Topics in Artificial Intelligence 2020, edited by Giovanni Volpe, Joana B. Pereira, Daniel Brunner, and Aydogan Ozcan. SPIE, 2020. http://dx.doi.org/10.1117/12.2571477.
Повний текст джерелаEtienne-Cummings, Ralph, Swati Mehta, Ralf Philipp, and Viktor Gruev. "Neuromorphic Vision Systems for Mobile Applications." In IEEE Custom Integrated Circuits Conference 2006. IEEE, 2006. http://dx.doi.org/10.1109/cicc.2006.320906.
Повний текст джерелаForsell, Jr., Robert, Allison L. Thornbrugh, and Carl A. Preyer. "Applications of smart neuromorphic focal planes." In San Dieg - DL Tentative, edited by John C. Carson. SPIE, 1990. http://dx.doi.org/10.1117/12.23006.
Повний текст джерелаЗвіти організацій з теми "Neuromorphic applications"
Davis, Joel L. Neuromorphic Systems: From Biological Foundations to System Properties and Real World Applications. Fort Belvoir, VA: Defense Technical Information Center, December 1997. http://dx.doi.org/10.21236/ada333498.
Повний текст джерелаPotok, Thomas, Catherine Schuman, Robert Patton, Todd Hylton, Hai Li, and Robinson Pino. Neuromorphic Computing, Architectures, Models, and Applications. A Beyond-CMOS Approach to Future Computing, June 29-July 1, 2016, Oak Ridge, TN. Office of Scientific and Technical Information (OSTI), December 2016. http://dx.doi.org/10.2172/1341738.
Повний текст джерела