Literatura académica sobre el tema "Neuromorphic platform"
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Artículos de revistas sobre el tema "Neuromorphic platform"
Urgese, Gianvito, Francesco Barchi, Emanuele Parisi, Evelina Forno, Andrea Acquaviva y Enrico Macii. "Benchmarking a Many-Core Neuromorphic Platform With an MPI-Based DNA Sequence Matching Algorithm". Electronics 8, n.º 11 (14 de noviembre de 2019): 1342. http://dx.doi.org/10.3390/electronics8111342.
Texto completoPerez-Peña, Fernando, M. Angeles Cifredo-Chacon y Angel Quiros-Olozabal. "Digital neuromorphic real-time platform". Neurocomputing 371 (enero de 2020): 91–99. http://dx.doi.org/10.1016/j.neucom.2019.09.004.
Texto completoRusso, Nicola, Haochun Huang, Eugenio Donati, Thomas Madsen y Konstantin Nikolic. "An Interface Platform for Robotic Neuromorphic Systems". Chips 2, n.º 1 (1 de febrero de 2023): 20–30. http://dx.doi.org/10.3390/chips2010002.
Texto completoWang, Junyi. "A Review of Spiking Neural Networks". SHS Web of Conferences 144 (2022): 03004. http://dx.doi.org/10.1051/shsconf/202214403004.
Texto completoZhai, Yongbiao, Peng Xie, Jiahui Hu, Xue Chen, Zihao Feng, Ziyu Lv, Guanglong Ding, Kui Zhou, Ye Zhou y Su-Ting Han. "Reconfigurable 2D-ferroelectric platform for neuromorphic computing". Applied Physics Reviews 10, n.º 1 (marzo de 2023): 011408. http://dx.doi.org/10.1063/5.0131838.
Texto completoBoldman, Walker L., Cheng Zhang, Thomas Z. Ward, Dayrl P. Briggs, Bernadeta R. Srijanto, Philip Brisk y Philip D. Rack. "Programmable Electrofluidics for Ionic Liquid Based Neuromorphic Platform". Micromachines 10, n.º 7 (17 de julio de 2019): 478. http://dx.doi.org/10.3390/mi10070478.
Texto completoTang, Jianbin, Benjamin Scott Mashford y Antonio Jimeno Yepes. "Semantic Labeling Using a Low-Power Neuromorphic Platform". IEEE Geoscience and Remote Sensing Letters 15, n.º 8 (agosto de 2018): 1184–88. http://dx.doi.org/10.1109/lgrs.2018.2834522.
Texto completoBose, Saurabh K., Joshua B. Mallinson, Edoardo Galli, Susant K. Acharya, Chloé Minnai, Philip J. Bones y Simon A. Brown. "Neuromorphic behaviour in discontinuous metal films". Nanoscale Horizons 7, n.º 4 (2022): 437–45. http://dx.doi.org/10.1039/d1nh00620g.
Texto completoSugiarto, Indar y Felix Pasila. "Understanding a Deep Learning Technique through a Neuromorphic System a Case Study with SpiNNaker Neuromorphic Platform". MATEC Web of Conferences 164 (2018): 01015. http://dx.doi.org/10.1051/matecconf/201816401015.
Texto completoPetrov, A., L. Alekseeva, A. Ivanov, V. Luchinin, A. Romanov, T. Chikyow y T. Nabatame. "On the way to a neuromorphic memristor computer platform". Nanoindustry Russia, n.º 1 (2016): 94–109. http://dx.doi.org/10.22184/1993-8578.2016.63.1.94.109.
Texto completoTesis sobre el tema "Neuromorphic platform"
Jeltsch, Sebastian [Verfasser] y Karlheinz [Akademischer Betreuer] Meier. "A Scalable Workflow for a Configurable Neuromorphic Platform / Sebastian Jeltsch ; Betreuer: Karlheinz Meier". Heidelberg : Universitätsbibliothek Heidelberg, 2014. http://d-nb.info/117992584X/34.
Texto completoFord, Andrew J. "LowPy: Simulation Platform for Machine Learning Algorithm Realization in Neuromorphic RRAM-Based Processors". University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617105323741119.
Texto completoNease, Stephen H. "Neural and analog computation on reconfigurable mixed-signal platforms". Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53999.
Texto completoFarahini, Nasim. "SiLago: Enabling System Level Automation Methodology to Design Custom High-Performance Computing Platforms : Toward Next Generation Hardware Synthesis Methodologies". Doctoral thesis, KTH, Elektronik och Inbyggda System, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-185787.
Texto completoWei-ChenHung y 洪瑋辰. "A deep learning simulation platform for non-volatile memory-based analog neuromorphic circuits". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/hwes23.
Texto completo國立成功大學
微電子工程研究所
107
With the rapid development of artificial intelligence, the Neuromorphic accelerator is regarded as a potential computing architecture in the future. Unlike the Von Neumann architecture, In-memory computing combines storage units and computing units on analog non-volatile memory. This method not only eliminates the time and energy consumption caused by the movement of data between the computing unit and the memory unit, but also make matrix multiplication to do large-scale parallelization, and finally achieve high efficiency energy consumption and reduce hardware area. In order to predict how the analog memory components under the new artificial intelligence architecture will affect the results such as accuracy, power consumption and operation speed, the goal of this paper is to establish a deep learning simulation platform for analogous non-volatile memory neuromorphic circuits. And explore the non-ideal characteristics of device such as bit constraints, nonlinear weight updates, component-to-component variations on neural network training. In this thesis, TensorFlow is used as the software framework to build a neural network simulation software. The mathematical function is used to describe the relationship between the number of analog device pulse and the weight. By modifying the parameters of the function, the bit precision of the device and degree of nonlinearity can be adjusted. In order to understand the influence of component variability on the neural network, a Gaussian distribution function is used to establish a variability distribution matrix, thereby simulating the device-to-device variation. In order to calculate the energy consumption of the synaptic array during the neural network training process, the formulas of dynamic energy consumption and static energy consumption are established, and the energy consumption in different operation stages is discussed. Finally, the parameters of the real device resistive memory (RRAM) are extracted to compare the accuracy of different device in the neural network. Using the simulation platform established by the above foundation, the simulation results show that the deivce needs at least 8-bit to achieve an accuracy of more than 90%. When the device curve more nonlinear, the accuracy decay more severe. By accumulating the weight gradient through additional digital circuits, the accuracy can achieve more than 95% in a low-precision neural network, and also greatly improve the accuracy of nonlinear characteristics. The results compare the accuracy of different real RRAM device in the neural network. In the variability simulation of component-to-component, it can be found that the neural network is robust to the variability of the device.
Capítulos de libros sobre el tema "Neuromorphic platform"
Chung, Daesu, Reid Hirata, T. Nathan Mundhenk, Jen Ng, Rob J. Peters, Eric Pichon, April Tsui et al. "A New Robotics Platform for Neuromorphic Vision: Beobots". En Biologically Motivated Computer Vision, 558–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36181-2_56.
Texto completoSugiarto, Indar, Agustinus Bimo Gumelar y Astri Yogatama. "Embedded Machine Learning on a Programmable Neuromorphic Platform". En Lecture Notes in Electrical Engineering, 119–28. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9781-4_13.
Texto completoCheng, Jingde. "Can “Neuromorphic Completeness” and “Brain-Inspired Computing” Provide a Promising Platform for Artificial General Intelligence?" En Advances in Intelligent Automation and Soft Computing, 111–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81007-8_14.
Texto completoShahsavari, Mahyar, Philippe Devienne y Pierre Boulet. "Spiking Neural Computing in Memristive Neuromorphic Platforms". En Handbook of Memristor Networks, 691–728. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-76375-0_25.
Texto completoKasabov, Nikola K. "From von Neumann Machines to Neuromorphic Platforms". En Springer Series on Bio- and Neurosystems, 661–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-57715-8_20.
Texto completoLi, Shiming, Lei Wang, Shiying Wang y Weixia Xu. "Liquid State Machine Applications Mapping for NoC-Based Neuromorphic Platforms". En Communications in Computer and Information Science, 277–89. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8135-9_20.
Texto completoBarchi, Francesco, Gianvito Urgese, Enrico Macii y Andrea Acquaviva. "Mapping Spiking Neural Networks on Multi-core Neuromorphic Platforms: Problem Formulation and Performance Analysis". En VLSI-SoC: Design and Engineering of Electronics Systems Based on New Computing Paradigms, 167–86. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23425-6_9.
Texto completoSoltani Zarrin, Pouya y Christian Wenger. "Pattern Recognition for COPD Diagnostics Using an Artificial Neural Network and Its Potential Integration on Hardware-Based Neuromorphic Platforms". En Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions, 284–88. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30493-5_29.
Texto completoZins, Noah, Yan Zhang y Hongyu An. "Implementation of Associative Memory Learning in Mobile Robots Using Neuromorphic Computing". En Neuromorphic Computing [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.110364.
Texto completo"Mixed-signal neuromorphic platform design for streaming biomedical signal processing". En Hardware Architectures for Deep Learning, 235–64. Institution of Engineering and Technology, 2020. http://dx.doi.org/10.1049/pbcs055e_ch10.
Texto completoActas de conferencias sobre el tema "Neuromorphic platform"
Zhou, Pujun y Shaogang Hu. "A Neuromorphic Computing Platform with Compact Neuromorphic Core". En 2021 IEEE 3rd International Conference on Circuits and Systems (ICCS). IEEE, 2021. http://dx.doi.org/10.1109/iccs52645.2021.9697293.
Texto completoBuckley, S. M., A. N. McCaughan, J. Chiles, R. P. Mirin, S. W. Nam y J. M. Shainline. "Superconducting optoelectronic platform for neuromorphic computing". En CLEO: Science and Innovations. Washington, D.C.: OSA, 2017. http://dx.doi.org/10.1364/cleo_si.2017.sth1n.3.
Texto completoHaessig, Germain, Francesco Galluppi, Xavier Lagorce y Ryad Benosman. "Neuromorphic networks on the SpiNNaker platform". En 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS). IEEE, 2019. http://dx.doi.org/10.1109/aicas.2019.8771512.
Texto completoSugiarto, Indar, Luis A. Plana, Steve Temple, Basabdatta S. Bhattacharya, Steve B. Furber y Patrick Camilleri. "Profiling a Many-core Neuromorphic Platform". En 2017 IEEE 11th International Conference on Application of Information and Communication Technologies (AICT). IEEE, 2017. http://dx.doi.org/10.1109/icaict.2017.8687014.
Texto completoDean, Mark E., Jason Chan, Christopher Daffron, Adam Disney, John Reynolds, Garrett Rose, James S. Plank, J. Douglas Birdwell y Catherine D. Schuman. "An Application Development Platform for neuromorphic computing". En 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727354.
Texto completoBui Phong, Nguyen Duc, Masoud Daneshtalab, Sergei Dytckov, Juha Plosila y Hannu Tenhunen. "Silicon synapse designs for VLSI neuromorphic platform". En 2014 NORCHIP. IEEE, 2014. http://dx.doi.org/10.1109/norchip.2014.7004745.
Texto completoEl Maghraoui, Kaoutar y Malte Rasch. "Platform for Next Generation Analog AI Hardware Acceleration Leveraging In-memory Computing Principals". En Neuromorphic Materials, Devices, Circuits and Systems. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2023. http://dx.doi.org/10.29363/nanoge.neumatdecas.2023.074.
Texto completoNease, Stephen, Stephen Brink y Paul Hasler. "STDP-enabled learning on a reconfigurable neuromorphic platform". En 2013 European Conference on Circuit Theory and Design (ECCTD). IEEE, 2013. http://dx.doi.org/10.1109/ecctd.2013.6662199.
Texto completoGalicia, Melvin, Farhad Merchant y Rainer Leupers. "A Parallel SystemC Virtual Platform for Neuromorphic Architectures". En 2022 23rd International Symposium on Quality Electronic Design (ISQED). IEEE, 2022. http://dx.doi.org/10.1109/isqed54688.2022.9806235.
Texto completoPark, Kicheol, Yena Lee, Jiman Hong, Jae-Hoon An y Bongjae Kim. "Selecting a Proper Neuromorphic Platform for the Intelligent IoT". En RACS '20: International Conference on Research in Adaptive and Convergent Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3400286.3418264.
Texto completoInformes sobre el tema "Neuromorphic platform"
Vineyard, Craig, Ryan Dellana, James Aimone y William Severa. Low-Power Deep Learning Inference using the SpiNNaker Neuromorphic Platform. Office of Scientific and Technical Information (OSTI), marzo de 2019. http://dx.doi.org/10.2172/1761866.
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