Letteratura scientifica selezionata sul tema "Neuromorphic technologies"
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Articoli di riviste sul tema "Neuromorphic technologies":
Okazaki, Atsuya. "Hardware Technologies for Neuromorphic Computing". Journal of the Robotics Society of Japan 35, n. 3 (2017): 209–14. http://dx.doi.org/10.7210/jrsj.35.209.
Argyris, Apostolos. "Photonic neuromorphic technologies in optical communications". Nanophotonics 11, n. 5 (19 gennaio 2022): 897–916. http://dx.doi.org/10.1515/nanoph-2021-0578.
Kim, Chul-Heung, Suhwan Lim, Sung Yun Woo, Won-Mook Kang, Young-Tak Seo, Sung-Tae Lee, Soochang Lee et al. "Emerging memory technologies for neuromorphic computing". Nanotechnology 30, n. 3 (13 novembre 2018): 032001. http://dx.doi.org/10.1088/1361-6528/aae975.
Varshika, M. Lakshmi, Federico Corradi e Anup Das. "Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends". Electronics 11, n. 10 (18 maggio 2022): 1610. http://dx.doi.org/10.3390/electronics11101610.
Della Rocca, Mattia. "Of the Artistic Nude and Technological Behaviorism". Nuncius 32, n. 2 (2017): 376–411. http://dx.doi.org/10.1163/18253911-03202006.
Rajendran, Bipin, e Fabien Alibart. "Neuromorphic Computing Based on Emerging Memory Technologies". IEEE Journal on Emerging and Selected Topics in Circuits and Systems 6, n. 2 (giugno 2016): 198–211. http://dx.doi.org/10.1109/jetcas.2016.2533298.
Woo, Jiyong, Jeong Hun Kim, Jong‐Pil Im e Seung Eon Moon. "Recent Advancements in Emerging Neuromorphic Device Technologies". Advanced Intelligent Systems 2, n. 10 (23 agosto 2020): 2000111. http://dx.doi.org/10.1002/aisy.202000111.
Woo, Jiyong, Jeong Hun Kim, Jong‐Pil Im e Seung Eon Moon. "Recent Advancements in Emerging Neuromorphic Device Technologies". Advanced Intelligent Systems 2, n. 10 (ottobre 2020): 2070101. http://dx.doi.org/10.1002/aisy.202070101.
Kurshan, Eren, Hai Li, Mingoo Seok e Yuan Xie. "A Case for 3D Integrated System Design for Neuromorphic Computing and AI Applications". International Journal of Semantic Computing 14, n. 04 (dicembre 2020): 457–75. http://dx.doi.org/10.1142/s1793351x20500063.
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, n. 1 (1 ottobre 2015): 000561–66. http://dx.doi.org/10.4071/isom-2015-tha15.
Tesi sul tema "Neuromorphic technologies":
Hock, Matthias [Verfasser], e Karlheinz [Akademischer Betreuer] Meier. "Modern Semiconductor Technologies for Neuromorphic Hardware / Matthias Hock ; Betreuer: Karlheinz Meier". Heidelberg : Universitätsbibliothek Heidelberg, 2014. http://d-nb.info/1180031628/34.
Jackson, Thomas C. "Building Efficient Neuromorphic Networks in Hardware with Mixed Signal Techniques and Emerging Technologies". Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1096.
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.
Garbin, Daniele. "Etude de la variabilité des technologies PCM et OxRAM pour leur utilisation en tant que synapses dans les systèmes neuromorphiques". Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAT133/document.
The human brain is made of a large number of interconnected neural networks which are composed of neurons and synapses. With a low power consumption of only few Watts, the human brain is able to perform computational tasks that are out of reach for today’s computers, which are based on the Von Neumann architecture. Neuromorphic hardware design, taking inspiration from the human brain, aims to implement the next generation, non-Von Neumann computing systems. In this thesis, emerging non-volatile memory devices, specifically Phase-Change Memory (PCM) and Oxide-based resistive memory (OxRAM) devices, are studied as artificial synapses in neuromorphic systems. The use of PCM devices as binary probabilistic synapses is studied for complex visual pattern extraction applications, evaluating the impact of the PCM programming conditions on the system-level power consumption.A programming strategy is proposed to mitigate the impact of PCM resistance drift. It is shown that, using scaled devices, it is possible to reduce the synaptic power consumption. The OxRAM resistance variability is evaluated experimentally through electrical characterization, gathering statistics on both single memory cells and at array level. A model that allows to reproduce OxRAM variability from low to high resistance state is developed. An OxRAM-based convolutional neural network architecture is then proposed on the basis of this experimental work. By implementing the computation of convolution directly in memory, the Von Neumann bottleneck is avoided. Robustness to OxRAM variability is demonstrated with complex visual pattern recognition tasks such as handwritten characters and traffic signs recognition
Ly, Denys. "Mémoires résistives et technologies 3D monolithiques pour processeurs neuromorphiques impulsionnels et reconfigurables". Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALT016.
The human brain is a complex, energy-efficient computational system that excels at cognitive tasks thanks to its natural capability to perform inference. By contrast, conventional computing systems based on the classic Von Neumann architecture require large power budget to execute such assignments. Herein comes the idea to build brain-inspired electronic computing systems, the so-called neuromorphic approach. In this thesis, we explore the use of novel technologies, namely Resistive Memories (RRAMs) and three-dimensional (3D) monolithic technologies, to enable the hardware implementation of compact, low-power reconfigurable Spiking Neural Network (SNN) processors. We first provide a comprehensive study of the impact of RRAM electrical properties on SNNs with RRAM synapses and trained with unsupervised learning (Spike-Timing-Dependent Plasticity (STDP)). In particular, we clarify the role of synaptic variability originating from RRAM resistance variability. Second, we investigate the use of RRAM-based Ternary Content-Addressable Memory (TCAM) arrays as synaptic routing tables in SNN processors to enable on-the-fly reconfigurability of network topology. For this purpose, we present in-depth electrical characterisations of two RRAM-based TCAM circuits: (i) the most common two-transistors/two-RRAMs (2T2R) RRAM-based TCAM, and (ii) a novel one-transistor/two-RRAMs/one-transistor (1T2R1T) RRAM-based TCAM, both featuring the smallest silicon area up-to-date. We compare both structures in terms of performance, reliability, and endurance. Finally, we explore the potential of 3D monolithic technologies to improve area-efficiency. In addition to the conventional monolithic integration of RRAMs in the back-end-of-line of CMOS technology, we examine the vertical stacking of CMOS over CMOS transistors. To this end, we demonstrate the full 3D monolithic integration of two tiers of CMOS transistors with one tier of RRAM devices, and present electrical characterisations performed on the fabricated devices
Suri, Manan. "Technologies émergentes de mémoire résistive pour les systèmes et application neuromorphique". Phd thesis, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00935190.
Janzakova, 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.
Neuromorphic 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
Haessig, Germain. "Neuromorphic computation using event-based sensors : from algorithms to hardware implementations". Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS422/document.
This thesis is about the implementation of neuromorphic algorithms, using, as a first step, data from a silicon retina, mimicking the human eye’s behavior, and then evolve towards all kind of event-based signals. These eventbased signals are coming from a paradigm shift in the data representation, thus allowing a high dynamic range, a precise temporal resolution and a sensor-level data compression. Especially, we will study the development of a high frequency monocular depth map generator, a real-time spike sorting algorithm for intelligent brain-machine interfaces, and an unsupervised learning algorithm for pattern recognition. Some of these algorithms (Optical flow detection, depth map construction from stereovision) will be in the meantime developed on available neuromorphic platforms (SpiNNaker, TrueNorth), thus allowing a fully neuromorphic pipeline, from sensing to computing, with a low power budget
Bedecarrats, Thomas. "Etude et intégration d’un circuit analogique, basse consommation et à faible surface d'empreinte, de neurone impulsionnel basé sur l’utilisation du BIMOS en technologie 28 nm FD-SOI". Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAT045.
While Moore’s law reaches its limits, microelectronics actors are looking for new paradigms to ensure future developments of our information society. Inspired by biologic nervous systems, neuromorphic engineering is providing new perspectives which have already enabled breakthroughs in artificial intelligence. To achieve sufficient performances to allow their spread, neural processors have to integrate neuron circuits as small and as low power(ed) as possible so that artificial neural networks they implement reach a critical size. In this work, we show that it is possible to reduce the number of components necessary to design an analogue spiking neuron circuit thanks to the functionalisation of parasitic generation currents in a BIMOS transistor integrated in 28 nm FD-SOI technology and sized with the minimum dimensions allowed by this technology. After a systematic characterization of the FD-SOI BIMOS currents under several biases through quasi-static measurements at room temperature, a compact model of this component, adapted from the CEA-LETI UTSOI one, is proposed. The BIMOS-based leaky, integrate-and-fire spiking neuron (BB-LIF SN) circuit is described. Influence of the different design and bias parameters on its behaviour observed during measurements performed on a demonstrator fabricated in silicon is explained in detail. A simple analytic model of its operating boundaries is proposed. The coherence between measurement and compact simulation results and predictions coming from the simple analytic model attests to the relevance of the proposed analysis. In its most successful achievement, the BB-LIF SN circuit is 15 µm², consumes around 2 pJ/spike, triggers at a rate between 3 and 75 kHz for 600 pA to 25 nA synaptic currents under a 3 V power supply
Cohen, Gregory Kevin. "Event-Based Feature Detection, Recognition and Classification". Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066204/document.
One of the fundamental tasks underlying much of computer vision is the detection, tracking and recognition of visual features. It is an inherently difficult and challenging problem, and despite the advances in computational power, pixel resolution, and frame rates, even the state-of-the-art methods fall far short of the robustness, reliability and energy consumption of biological vision systems. Silicon retinas, such as the Dynamic Vision Sensor (DVS) and Asynchronous Time-based Imaging Sensor (ATIS), attempt to replicate some of the benefits of biological retinas and provide a vastly different paradigm in which to sense and process the visual world. Tasks such as tracking and object recognition still require the identification and matching of local visual features, but the detection, extraction and recognition of features requires a fundamentally different approach, and the methods that are commonly applied to conventional imaging are not directly applicable. This thesis explores methods to detect features in the spatio-temporal information from event-based vision sensors. The nature of features in such data is explored, and methods to determine and detect features are demonstrated. A framework for detecting, tracking, recognising and classifying features is developed and validated using real-world data and event-based variations of existing computer vision datasets and benchmarks. The results presented in this thesis demonstrate the potential and efficacy of event-based systems. This work provides an in-depth analysis of different event-based methods for object recognition and classification and introduces two feature-based methods. Two learning systems, one event-based and the other iterative, were used to explore the nature and classification ability of these methods. The results demonstrate the viability of event-based classification and the importance and role of motion in event-based feature detection
Libri sul tema "Neuromorphic technologies":
Pearce, Tim C. Chemosensation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0017.
Capitoli di libri sul tema "Neuromorphic technologies":
Saïghi, Sylvain. "Neuromorphic Technologies, Memristors". In Encyclopedia of Computational Neuroscience, 2001–3. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_116.
Saïghi, Sylvain. "Neuromorphic Technologies, Memristors". In Encyclopedia of Computational Neuroscience, 1–3. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_116-1.
Lim, Gerard Joseph, Calvin Ching Ian Ang e Wen Siang Lew. "Spintronics for Neuromorphic Engineering". In Emerging Non-volatile Memory Technologies, 297–315. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-6912-8_9.
Dananjaya, Putu Andhita, Roshan Gopalakrishnan e Wen Siang Lew. "RRAM-Based Neuromorphic Computing Systems". In Emerging Non-volatile Memory Technologies, 383–414. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-6912-8_12.
Narduzzi, Simon, Loreto Mateu, Petar Jokic, Erfan Azarkhish e 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.
Li, Zheng, Chenchen Liu, Hai Li e Yiran Chen. "Neuromorphic Hardware Acceleration Enabled by Emerging Technologies". In Emerging Technology and Architecture for Big-data Analytics, 217–44. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54840-1_10.
Molendijk, Maarten, Kanishkan Vadivel, Federico Corradi, Gert-Jan van Schaik, Amirreza Yousefzadeh e Henk Corporaal. "Benchmarking the Epiphany Processor as a Reference Neuromorphic Architecture". In Industrial Artificial Intelligence Technologies and Applications, 21–34. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003377382-2.
Sengupta, Abhronil, Aayush Ankit e Kaushik Roy. "Efficient Neuromorphic Systems and Emerging Technologies: Prospects and Perspectives". In Emerging Technology and Architecture for Big-data Analytics, 261–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54840-1_12.
Asad, Arghavan, e Farah Mohammadi. "NeuroTower: A 3D Neuromorphic Architecture with Low-Power TSVs". In Proceedings of the Future Technologies Conference (FTC) 2022, Volume 3, 227–36. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18344-7_14.
Narduzzi, Simon, Dorvan Favre, Nuria Pazos Escudero e L. Andrea Dunbar. "Deploying a Convolutional Neural Network on Edge MCU and Neuromorphic Hardware Platforms". In Industrial Artificial Intelligence Technologies and Applications, 129–39. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003377382-10.
Atti di convegni sul tema "Neuromorphic technologies":
Kirkland, Paul, Gaetano Di Caterina, John Soraghan e George Matich. "Neuromorphic technologies for defence and security". In Emerging Imaging and Sensing Technologies for Security and Defence V; Advanced Manufacturing Technologies for Micro- and Nanosystems in Security and Defence III, a cura di Maria Farsari, John G. Rarity, Francois Kajzar, Attila Szep, Richard C. Hollins, Gerald S. Buller, Robert A. Lamb et al. SPIE, 2020. http://dx.doi.org/10.1117/12.2575978.
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.
Ielmini, Daniele. "Embedded memory technologies for neuromorphic computing". In Neuronics Conference. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2023. http://dx.doi.org/10.29363/nanoge.neuronics.2024.029.
Narayanan, Pritish, Geoffrey W. Burr, Stefano Ambrogio e Robert M. Shelby. "Neuromorphic Technologies for Next-Generation Cognitive Computing". In 2017 IEEE International Memory Workshop (IMW). IEEE, 2017. http://dx.doi.org/10.1109/imw.2017.7939095.
Shelby, Robert M., Pritish Narayanan, Stefano Ambrogio, Hsinyu Tsai, Kohji Hosokawa, Scott C. Lewis e 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.
Andrishak, Artur, Tiago L. Alves, Ricardo M. R. Adão, Christian Maibohm, Bruno Romeira e Jana B. Nieder. "3D Polymer Interconnects for Neuromorphic Photonics Technologies". In 2023 Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference (CLEO/Europe-EQEC). IEEE, 2023. http://dx.doi.org/10.1109/cleo/europe-eqec57999.2023.10232683.
Stark, P., J. Weiss, R. Dangel, F. Horst, J. Geler-Kremer e B. J. Offrein. "High-Performance Neuromorphic Computing Based on Photonic Technologies". In Optical Fiber Communication Conference. Washington, D.C.: OSA, 2021. http://dx.doi.org/10.1364/ofc.2021.tu5h.4.
Hai Li. "Conventional and neuromorphic systems leveraging emerging memory technologies". In 2017 International Symposium on VLSI Design, Automation and Test (VLSI-DAT). IEEE, 2017. http://dx.doi.org/10.1109/vlsi-dat.2017.7939673.
Offrein, 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.
Mishra, Vishwas, Abhishek Kumar e Shyam Akashe. "New Non-Volatile Memory Technologies and Neuromorphic Computing". In 2023 IEEE World Conference on Applied Intelligence and Computing (AIC). IEEE, 2023. http://dx.doi.org/10.1109/aic57670.2023.10263872.