Auswahl der wissenschaftlichen Literatur zum Thema „Neuromorphic technologies/devices“
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Zeitschriftenartikel zum Thema "Neuromorphic technologies/devices"
Orii, Yasumitsu, Akihiro Horibe, Kuniaki Sueoka, Keiji Matsumoto, Toyohiro Aoki, Hirokazu Noma, Sayuri Kohara et al. „PERSPECTIVE ON REQUIRED PACKAGING TECHNOLOGIES FOR NEUROMORPHIC DEVICES“. International Symposium on Microelectronics 2015, Nr. 1 (01.10.2015): 000561–66. http://dx.doi.org/10.4071/isom-2015-tha15.
Der volle Inhalt der QuelleDiao, Yu, Yaoxuan Zhang, Yanran Li und Jie Jiang. „Metal-Oxide Heterojunction: From Material Process to Neuromorphic Applications“. Sensors 23, Nr. 24 (12.12.2023): 9779. http://dx.doi.org/10.3390/s23249779.
Der volle Inhalt der QuelleMilo, Valerio, Gerardo Malavena, Christian Monzio Compagnoni und Daniele Ielmini. „Memristive and CMOS Devices for Neuromorphic Computing“. Materials 13, Nr. 1 (01.01.2020): 166. http://dx.doi.org/10.3390/ma13010166.
Der volle Inhalt der QuelleAbbas, Haider, Jiayi Li und Diing Shenp Ang. „Conductive Bridge Random Access Memory (CBRAM): Challenges and Opportunities for Memory and Neuromorphic Computing Applications“. Micromachines 13, Nr. 5 (30.04.2022): 725. http://dx.doi.org/10.3390/mi13050725.
Der volle Inhalt der QuelleAllwood, Dan A., Matthew O. A. Ellis, David Griffin, Thomas J. Hayward, Luca Manneschi, Mohammad F. KH Musameh, Simon O'Keefe et al. „A perspective on physical reservoir computing with nanomagnetic devices“. Applied Physics Letters 122, Nr. 4 (23.01.2023): 040501. http://dx.doi.org/10.1063/5.0119040.
Der volle Inhalt der QuelleDella Rocca, Mattia. „Of the Artistic Nude and Technological Behaviorism“. Nuncius 32, Nr. 2 (2017): 376–411. http://dx.doi.org/10.1163/18253911-03202006.
Der volle Inhalt der QuelleKurshan, Eren, Hai Li, Mingoo Seok und Yuan Xie. „A Case for 3D Integrated System Design for Neuromorphic Computing and AI Applications“. International Journal of Semantic Computing 14, Nr. 04 (Dezember 2020): 457–75. http://dx.doi.org/10.1142/s1793351x20500063.
Der volle Inhalt der QuelleHajtó, Dániel, Ádám Rák und György Cserey. „Robust Memristor Networks for Neuromorphic Computation Applications“. Materials 12, Nr. 21 (31.10.2019): 3573. http://dx.doi.org/10.3390/ma12213573.
Der volle Inhalt der QuelleCovi, Erika, Halid Mulaosmanovic, Benjamin Max, Stefan Slesazeck und Thomas Mikolajick. „Ferroelectric-based synapses and neurons for neuromorphic computing“. Neuromorphic Computing and Engineering 2, Nr. 1 (07.02.2022): 012002. http://dx.doi.org/10.1088/2634-4386/ac4918.
Der volle Inhalt der QuelleSueoka, Brandon, und Feng Zhao. „Memristive synaptic device based on a natural organic material—honey for spiking neural network in biodegradable neuromorphic systems“. Journal of Physics D: Applied Physics 55, Nr. 22 (07.03.2022): 225105. http://dx.doi.org/10.1088/1361-6463/ac585b.
Der volle Inhalt der QuelleDissertationen zum Thema "Neuromorphic technologies/devices"
Calayir, Vehbi. „Neurocomputing and Associative Memories Based on Emerging Technologies: Co-optimization of Technology and Architecture“. Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/422.
Der volle Inhalt der QuelleJanzakova, Kamila. „Développement de dendrites polymères organiques en 3D comme dispositif neuromorphique“. Electronic Thesis or Diss., Université de Lille (2022-....), 2023. http://www.theses.fr/2023ULILN017.
Der volle Inhalt der QuelleNeuromorphic technologies is a promising direction for development of more advanced and energy-efficient computing. They aim to replicate attractive brain features such as high computational efficiency at low power consumption on a software and hardware level. At the moment, brain-inspired software implementations (such as ANN and SNN) have already shown their successful application for different types of tasks (image and speech recognition). However, to benefit more from the brain-like algorithms, one may combine them with appropriate hardware that would also rely on brain-like architecture and processes and thus complement them. Neuromorphic engineering has already shown the utilization of solid-state electronics (CMOS circuits, memristor) for the development of brain-inspired devices. Nevertheless, these implementations are fabricated through top-down methods. In contrast, brain computing relies on bottom-up processes such as interconnectivity between cells and the formation of neural communication pathways.In the light of mentioned above, this work reports on the development of programmable 3D organic neuromorphic devices, which, unlike most current neuromorphic technologies, can be created in a bottom-up manner. This allows bringing neuromorphic technologies closer to the level of brain programming, where necessary neural paths are established only on the need.First, we found out that PEDOT:PSS based 3D interconnections can be formed by means of AC-bipolar electropolymerization and that they are capable of mimicking the growth of neural cells. By tuning individually the parameters of the waveform (peak amplitude voltage -VP, frequency - f, duty cycle - dc and offset voltage - Voff), a wide range of dendrite-like structures was observed with various branching degrees, volumes, surface areas, asymmetry of formation, and even growth dynamics.Next, it was discovered that dendritic morphologies obtained at various frequencies are conductive. Moreover, each structure exhibits an individual conductance value that can be interpreted as synaptic weight. More importantly, the ability of dendrites to function as OECT was revealed. Different dendrites exhibited different performances as OECT. Further, the ability of PEDOT:PSS dendrites to change their conductivity in response to gate voltage was used to mimic brain memory functions (short-term plasticity -STP and long-term plasticity -LTP). STP responses varied depending on the dendritic structure. Moreover, emulation of LTP was demonstrated not only by means of an Ag/AgCl gate wire but as well by means of a self-developed polymer dendritic gate.Finally, structural plasticity was demonstrated through dendritic growth, where the weight of the final connection is governed according to Hebbian learning rules (spike-timing-dependent plasticity - STDP and spike-rate-dependent plasticity - SRDP). Using both approaches, a variety of dendritic topologies with programmable conductance states (i.e., synaptic weight) and various dynamics of growth have been observed. Eventually, using the same dendritic structural plasticity, more complex brain features such as associative learning and classification tasks were emulated.Additionally, future perspectives of such technologies based on self-propagating polymer dendritic objects were discussed
Buchteile zum Thema "Neuromorphic technologies/devices"
Ricci, Saverio, Piergiulio Mannocci, Matteo Farronato, Alessandro Milozzi und Daniele Ielmini. „Development of Crosspoint Memory Arrays for Neuromorphic Computing“. In Special Topics in Information Technology, 65–74. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51500-2_6.
Der volle Inhalt der QuelleCarstens, Niko, Maik-Ivo Terasa, Pia Holtz, Sören Kaps, Thomas Strunskus, Abdou Hassanien, Rainer Adelung, Franz Faupel und Alexander Vahl. „Memristive Switching: From Individual Nanoparticles Towards Complex Nanoparticle Networks“. In Springer Series on Bio- and Neurosystems, 219–39. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36705-2_9.
Der volle Inhalt der QuelleWalters, B., C. Lammie, J. Eshraghian, C. Yakopcic, T. Taha, R. Genov, M. V. Jacob, A. Amirsoleimani und M. R. Azghadi. „Memristive Devices for Neuromorphic and Deep Learning Applications“. In Advanced Memory Technology, 680–704. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781839169946-00680.
Der volle Inhalt der QuelleShanbogh, Shobith M., R. Anju Kumari und Ponnam Anjaneyulu. „Hybrid Devices for Neuromorphic Applications“. In Advanced Memory Technology, 622–55. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781839169946-00622.
Der volle Inhalt der QuelleAhmed, T., V. Krishnamurthi und S. Walia. „Working Dynamics in Low-dimensional Material-based Neuromorphic Devices“. In Advanced Memory Technology, 458–97. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781839169946-00458.
Der volle Inhalt der QuelleYang, Chaofei, Hai Li und Yiran Chen. „Nanoscale Memory Architectures for Neuromorphic Computing“. In Security Opportunities in Nano Devices and Emerging Technologies, 215–34. CRC Press, 2017. http://dx.doi.org/10.1201/9781315265056-12.
Der volle Inhalt der QuelleAhmed, L. Jubair, S. Dhanasekar, K. Martin Sagayam, Surbhi Vijh, Vipin Tyagi, Mayank Singh und Alex Norta. „Introduction to Neuromorphic Computing Systems“. In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 1–29. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6596-7.ch001.
Der volle Inhalt der QuelleZhuang, Yanling, Shujuan Liu und Qiang Zhao. „Organic Resistive Memories for Neuromorphic Electronics“. In Advanced Memory Technology, 60–120. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781839169946-00060.
Der volle Inhalt der QuelleZanotti, Tommaso, Paolo Pavan und Francesco Maria Puglisi. „Study of RRAM-Based Binarized Neural Networks Inference Accelerators Using an RRAM Physics-Based Compact Model“. In Neuromorphic Computing [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.110340.
Der volle Inhalt der QuellePereira, M. E., E. Carlos, E. Fortunato, R. Martins, P. Barquinha und A. Kiazadeh. „Amorphous Oxide Semiconductor Memristors: Brain-inspired Computation“. In Advanced Memory Technology, 431–57. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781839169946-00431.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Neuromorphic technologies/devices"
Strukov, D. „Emerging Memory Technologies for Neuromorphic Computing“. In 2016 International Conference on Solid State Devices and Materials. The Japan Society of Applied Physics, 2016. http://dx.doi.org/10.7567/ssdm.2016.b-7-02.
Der volle Inhalt der QuelleShelby, Robert M., Pritish Narayanan, Stefano Ambrogio, Hsinyu Tsai, Kohji Hosokawa, Scott C. Lewis und Geoffrey W. Burr. „Neuromorphic technologies for next-generation cognitive computing“. In 2017 IEEE Electron Devices Technology and Manufacturing Conference (EDTM). IEEE, 2017. http://dx.doi.org/10.1109/edtm.2017.7947500.
Der volle Inhalt der QuelleLee, Sungsik. „Amorphous oxide thin-film devices for neuromorphic applications“. In Advances in Display Technologies XII, herausgegeben von Jiun-Haw Lee, Qiong-Hua Wang und Tae-Hoon Yoon. SPIE, 2022. http://dx.doi.org/10.1117/12.2612015.
Der volle Inhalt der QuelleShastri, Bhavin J., Thomas Ferreira de Lima, Alexander N. Tait, Bicky A. Marquez, Hsuan-Tung Peng, Chaoran Huang, Volker J. Sorger und Paul R. Prucnal. „Advances in neuromorphic photonics (Conference Presentation)“. In Integrated Optics: Devices, Materials, and Technologies XXIV, herausgegeben von Sonia M. García-Blanco und Pavel Cheben. SPIE, 2020. http://dx.doi.org/10.1117/12.2554476.
Der volle Inhalt der QuelleOffrein, Bert Jan, Tommaso Stecconi, Donato Francesco Falcone, Elger Anne Vlieg, Felix Hermann, Laura Bégon-Lours, Daniel Jubin et al. „Photonic and electronic integrated technologies for neuromorphic computing“. In 2023 International Conference on Solid State Devices and Materials. The Japan Society of Applied Physics, 2023. http://dx.doi.org/10.7567/ssdm.2023.h-2-01.
Der volle Inhalt der QuelleDabos, George, George Mourgias-Alexandris, Angelina Totovic, Manos Kirtas, Nikos Passalis, Anastasios Tefas und Nikos Pleros. „End-to-end deep learning with neuromorphic photonics“. In Integrated Optics: Devices, Materials, and Technologies XXV, herausgegeben von Sonia M. García-Blanco und Pavel Cheben. SPIE, 2021. http://dx.doi.org/10.1117/12.2587668.
Der volle Inhalt der QuellePolnau, Ernst E., und Mikhail Vorontsov. „Atmospheric turbulence characterization using a neuromorphic camera“. In Image Sensing Technologies: Materials, Devices, Systems, and Applications IX, herausgegeben von K. Kay Son, Nibir K. Dhar, Achyut K. Dutta und Sachidananda R. Babu. SPIE, 2022. http://dx.doi.org/10.1117/12.2618894.
Der volle Inhalt der QuellePhillips, Matthew E., Nigel D. Stepp, Jose Cruz-Albrecht, Vincent De Sapio, Tsai-Ching Lu und Vincent Sritapan. „Neuromorphic and early warning behavior-based authentication for mobile devices“. In 2016 IEEE Symposium on Technologies for Homeland Security (HST). IEEE, 2016. http://dx.doi.org/10.1109/ths.2016.7568965.
Der volle Inhalt der QuelleYoo, S. J. Ben. „Intelligent imaging microsystems realized by 3D electronic-photonic integrated circuits with embedded neuromorphic computing“. In Image Sensing Technologies: Materials, Devices, Systems, and Applications XI, herausgegeben von Nibir K. Dhar, Achyut K. Dutta und Sachidananda R. Babu. SPIE, 2024. http://dx.doi.org/10.1117/12.3013294.
Der volle Inhalt der QuelleRobinson, Michael G., Lin Zhang, Kristina M. Johnson und David A. Jared. „Custom electro-optic devices for optically implemented neuromorphic computing systems“. In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.mvv9.
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