Добірка наукової літератури з теми "Neuromorphic platform"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Neuromorphic platform".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Neuromorphic platform"
Urgese, Gianvito, Francesco Barchi, Emanuele Parisi, Evelina Forno, Andrea Acquaviva, and Enrico Macii. "Benchmarking a Many-Core Neuromorphic Platform With an MPI-Based DNA Sequence Matching Algorithm." Electronics 8, no. 11 (November 14, 2019): 1342. http://dx.doi.org/10.3390/electronics8111342.
Повний текст джерелаPerez-Peña, Fernando, M. Angeles Cifredo-Chacon, and Angel Quiros-Olozabal. "Digital neuromorphic real-time platform." Neurocomputing 371 (January 2020): 91–99. http://dx.doi.org/10.1016/j.neucom.2019.09.004.
Повний текст джерелаRusso, Nicola, Haochun Huang, Eugenio Donati, Thomas Madsen, and Konstantin Nikolic. "An Interface Platform for Robotic Neuromorphic Systems." Chips 2, no. 1 (February 1, 2023): 20–30. http://dx.doi.org/10.3390/chips2010002.
Повний текст джерелаWang, Junyi. "A Review of Spiking Neural Networks." SHS Web of Conferences 144 (2022): 03004. http://dx.doi.org/10.1051/shsconf/202214403004.
Повний текст джерелаZhai, Yongbiao, Peng Xie, Jiahui Hu, Xue Chen, Zihao Feng, Ziyu Lv, Guanglong Ding, Kui Zhou, Ye Zhou, and Su-Ting Han. "Reconfigurable 2D-ferroelectric platform for neuromorphic computing." Applied Physics Reviews 10, no. 1 (March 2023): 011408. http://dx.doi.org/10.1063/5.0131838.
Повний текст джерелаBoldman, Walker L., Cheng Zhang, Thomas Z. Ward, Dayrl P. Briggs, Bernadeta R. Srijanto, Philip Brisk, and Philip D. Rack. "Programmable Electrofluidics for Ionic Liquid Based Neuromorphic Platform." Micromachines 10, no. 7 (July 17, 2019): 478. http://dx.doi.org/10.3390/mi10070478.
Повний текст джерелаTang, Jianbin, Benjamin Scott Mashford, and Antonio Jimeno Yepes. "Semantic Labeling Using a Low-Power Neuromorphic Platform." IEEE Geoscience and Remote Sensing Letters 15, no. 8 (August 2018): 1184–88. http://dx.doi.org/10.1109/lgrs.2018.2834522.
Повний текст джерелаBose, Saurabh K., Joshua B. Mallinson, Edoardo Galli, Susant K. Acharya, Chloé Minnai, Philip J. Bones, and Simon A. Brown. "Neuromorphic behaviour in discontinuous metal films." Nanoscale Horizons 7, no. 4 (2022): 437–45. http://dx.doi.org/10.1039/d1nh00620g.
Повний текст джерелаSugiarto, Indar, and 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.
Повний текст джерелаPetrov, A., L. Alekseeva, A. Ivanov, V. Luchinin, A. Romanov, T. Chikyow, and T. Nabatame. "On the way to a neuromorphic memristor computer platform." Nanoindustry Russia, no. 1 (2016): 94–109. http://dx.doi.org/10.22184/1993-8578.2016.63.1.94.109.
Повний текст джерелаДисертації з теми "Neuromorphic platform"
Jeltsch, Sebastian [Verfasser], and 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.
Повний текст джерелаFord, 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.
Повний текст джерелаNease, Stephen H. "Neural and analog computation on reconfigurable mixed-signal platforms." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53999.
Повний текст джерелаFarahini, 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.
Повний текст джерелаWei-ChenHung and 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.
Повний текст джерела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.
Частини книг з теми "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." In Biologically Motivated Computer Vision, 558–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36181-2_56.
Повний текст джерелаSugiarto, Indar, Agustinus Bimo Gumelar, and Astri Yogatama. "Embedded Machine Learning on a Programmable Neuromorphic Platform." In Lecture Notes in Electrical Engineering, 119–28. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9781-4_13.
Повний текст джерелаCheng, Jingde. "Can “Neuromorphic Completeness” and “Brain-Inspired Computing” Provide a Promising Platform for Artificial General Intelligence?" In 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.
Повний текст джерелаShahsavari, Mahyar, Philippe Devienne, and Pierre Boulet. "Spiking Neural Computing in Memristive Neuromorphic Platforms." In Handbook of Memristor Networks, 691–728. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-76375-0_25.
Повний текст джерелаKasabov, Nikola K. "From von Neumann Machines to Neuromorphic Platforms." In 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.
Повний текст джерелаLi, Shiming, Lei Wang, Shiying Wang, and Weixia Xu. "Liquid State Machine Applications Mapping for NoC-Based Neuromorphic Platforms." In Communications in Computer and Information Science, 277–89. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8135-9_20.
Повний текст джерелаBarchi, Francesco, Gianvito Urgese, Enrico Macii, and Andrea Acquaviva. "Mapping Spiking Neural Networks on Multi-core Neuromorphic Platforms: Problem Formulation and Performance Analysis." In 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.
Повний текст джерелаSoltani Zarrin, Pouya, and Christian Wenger. "Pattern Recognition for COPD Diagnostics Using an Artificial Neural Network and Its Potential Integration on Hardware-Based Neuromorphic Platforms." In 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.
Повний текст джерелаZins, Noah, Yan Zhang, and Hongyu An. "Implementation of Associative Memory Learning in Mobile Robots Using Neuromorphic Computing." In Neuromorphic Computing [Working Title]. IntechOpen, 2023. http://dx.doi.org/10.5772/intechopen.110364.
Повний текст джерела"Mixed-signal neuromorphic platform design for streaming biomedical signal processing." In Hardware Architectures for Deep Learning, 235–64. Institution of Engineering and Technology, 2020. http://dx.doi.org/10.1049/pbcs055e_ch10.
Повний текст джерелаТези доповідей конференцій з теми "Neuromorphic platform"
Zhou, Pujun, and Shaogang Hu. "A Neuromorphic Computing Platform with Compact Neuromorphic Core." In 2021 IEEE 3rd International Conference on Circuits and Systems (ICCS). IEEE, 2021. http://dx.doi.org/10.1109/iccs52645.2021.9697293.
Повний текст джерелаBuckley, S. M., A. N. McCaughan, J. Chiles, R. P. Mirin, S. W. Nam, and J. M. Shainline. "Superconducting optoelectronic platform for neuromorphic computing." In CLEO: Science and Innovations. Washington, D.C.: OSA, 2017. http://dx.doi.org/10.1364/cleo_si.2017.sth1n.3.
Повний текст джерелаHaessig, Germain, Francesco Galluppi, Xavier Lagorce, and Ryad Benosman. "Neuromorphic networks on the SpiNNaker platform." In 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS). IEEE, 2019. http://dx.doi.org/10.1109/aicas.2019.8771512.
Повний текст джерелаSugiarto, Indar, Luis A. Plana, Steve Temple, Basabdatta S. Bhattacharya, Steve B. Furber, and Patrick Camilleri. "Profiling a Many-core Neuromorphic Platform." In 2017 IEEE 11th International Conference on Application of Information and Communication Technologies (AICT). IEEE, 2017. http://dx.doi.org/10.1109/icaict.2017.8687014.
Повний текст джерелаDean, Mark E., Jason Chan, Christopher Daffron, Adam Disney, John Reynolds, Garrett Rose, James S. Plank, J. Douglas Birdwell, and Catherine D. Schuman. "An Application Development Platform for neuromorphic computing." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727354.
Повний текст джерелаBui Phong, Nguyen Duc, Masoud Daneshtalab, Sergei Dytckov, Juha Plosila, and Hannu Tenhunen. "Silicon synapse designs for VLSI neuromorphic platform." In 2014 NORCHIP. IEEE, 2014. http://dx.doi.org/10.1109/norchip.2014.7004745.
Повний текст джерелаEl Maghraoui, Kaoutar, and Malte Rasch. "Platform for Next Generation Analog AI Hardware Acceleration Leveraging In-memory Computing Principals." In Neuromorphic Materials, Devices, Circuits and Systems. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2023. http://dx.doi.org/10.29363/nanoge.neumatdecas.2023.074.
Повний текст джерелаNease, Stephen, Stephen Brink, and Paul Hasler. "STDP-enabled learning on a reconfigurable neuromorphic platform." In 2013 European Conference on Circuit Theory and Design (ECCTD). IEEE, 2013. http://dx.doi.org/10.1109/ecctd.2013.6662199.
Повний текст джерелаGalicia, Melvin, Farhad Merchant, and Rainer Leupers. "A Parallel SystemC Virtual Platform for Neuromorphic Architectures." In 2022 23rd International Symposium on Quality Electronic Design (ISQED). IEEE, 2022. http://dx.doi.org/10.1109/isqed54688.2022.9806235.
Повний текст джерелаPark, Kicheol, Yena Lee, Jiman Hong, Jae-Hoon An, and Bongjae Kim. "Selecting a Proper Neuromorphic Platform for the Intelligent IoT." In 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.
Повний текст джерелаЗвіти організацій з теми "Neuromorphic platform"
Vineyard, Craig, Ryan Dellana, James Aimone, and William Severa. Low-Power Deep Learning Inference using the SpiNNaker Neuromorphic Platform. Office of Scientific and Technical Information (OSTI), March 2019. http://dx.doi.org/10.2172/1761866.
Повний текст джерела