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Relevant bibliographies by topics / Neuromorphic platform

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Academic literature on the topic 'Neuromorphic platform'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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Journal articles on the topic "Neuromorphic platform"

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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 (2019): 1342. http://dx.doi.org/10.3390/electronics8111342.

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SpiNNaker is a neuromorphic globally asynchronous locally synchronous (GALS) multi-core architecture designed for simulating a spiking neural network (SNN) in real-time. Several studies have shown that neuromorphic platforms allow flexible and efficient simulations of SNN by exploiting the efficient communication infrastructure optimised for transmitting small packets across the many cores of the platform. However, the effectiveness of neuromorphic platforms in executing massively parallel general-purpose algorithms, while promising, is still to be explored. In this paper, we present an implem

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Perez-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.

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Chen, Guang, Jian Cao, Chenglong Zou, et al. "PAIBoard: A Neuromorphic Computing Platform for Hybrid Neural Networks in Robot Dog Application." Electronics 13, no. 18 (2024): 3619. http://dx.doi.org/10.3390/electronics13183619.

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Hybrid neural networks (HNNs), integrating the strengths of artificial neural networks (ANNs) and spiking neural networks (SNNs), provide a promising solution towards generic artificial intelligence. There is a prevailing trend towards designing unified SNN-ANN paradigm neuromorphic computing chips to support HNNs, but developing platforms to advance neuromorphic computing systems is equally essential. This paper presents the PAIBoard platform, which is designed to facilitate the implementation of HNNs. The platform comprises three main components: the upper computer, the communication module,

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Russo, Nicola, Haochun Huang, Eugenio Donati, Thomas Madsen, and Konstantin Nikolic. "An Interface Platform for Robotic Neuromorphic Systems." Chips 2, no. 1 (2023): 20–30. http://dx.doi.org/10.3390/chips2010002.

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Neuromorphic computing is promising to become a future standard in low-power AI applications. The integration between new neuromorphic hardware and traditional microcontrollers is an open challenge. In this paper, we present an interface board and a communication protocol that allows communication between different devices, using a microcontroller unit (Arduino Due) in the middle. Our compact printed circuit board (PCB) links different devices as a whole system and provides a power supply for the entire system using batteries as the power supply. Concretely, we have connected a Dynamic Vision

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Neumann, Adam. "Advancements in Unsupervised Learning: Mode-Assisted Quantum Restricted Boltzmann Machines Leveraging Neuromorphic Computing on the Dynex Platform." International Journal of Bioinformatics and Intelligent Computing 3, no. 1 (2024): 91–103. http://dx.doi.org/10.61797/ijbic.v3i1.300.

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The integration of neuromorphic computing into the Dynex platform signifies a transformative step in computational technology, particularly in the realms of machine learning and optimization. This advanced platform leverages the unique attributes of neuromorphic dynamics, utilizing neuromorphic annealing - a technique divergent from conventional computing methods - to adeptly address intricate problems in discrete optimization, sampling, and machine learning. Our research concentrates on enhancing the training process of Restricted Boltzmann Machines (RBMs), a category of generative models tra

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Wang, Junyi. "A Review of Spiking Neural Networks." SHS Web of Conferences 144 (2022): 03004. http://dx.doi.org/10.1051/shsconf/202214403004.

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Spiking neuron network (SNN) attaches much attention to researchers in neuromorphic engineering and brain-like computing because of its advantages in Spatio-temporal dynamics, diverse coding mechanisms, and event-driven properties. This paper is a review of SNN in order to help researchers from other areas to know and became familiar with the field of SNN or even became interested in SNN. Neuron models, coding methods, training algorithms, and neuromorphic computing platforms will be introduced in this paper. This paper analyzes the disadvantages and advantages of several kinds of neural model

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Al Abdul Wahid, Seham, Arghavan Asad, and Farah Mohammadi. "A Survey on Neuromorphic Architectures for Running Artificial Intelligence Algorithms." Electronics 13, no. 15 (2024): 2963. http://dx.doi.org/10.3390/electronics13152963.

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Neuromorphic computing, a brain-inspired non-Von Neumann computing system, addresses the challenges posed by the Moore’s law memory wall phenomenon. It has the capability to enhance performance while maintaining power efficiency. Neuromorphic chip architecture requirements vary depending on the application and optimising it for large-scale applications remains a challenge. Neuromorphic chips are programmed using spiking neural networks which provide them with important properties such as parallelism, asynchronism, and on-device learning. Widely used spiking neuron models include the Hodgkin–Hu

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Zhai, Yongbiao, Peng Xie, Jiahui Hu, et al. "Reconfigurable 2D-ferroelectric platform for neuromorphic computing." Applied Physics Reviews 10, no. 1 (2023): 011408. http://dx.doi.org/10.1063/5.0131838.

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To meet the requirement of data-intensive computing in the data-explosive era, brain-inspired neuromorphic computing have been widely investigated for the last decade. However, incompatible preparation processes severely hinder the cointegration of synaptic and neuronal devices in a single chip, which limited the energy-efficiency and scalability. Therefore, developing a reconfigurable device including synaptic and neuronal functions in a single chip with same homotypic materials and structures is highly desired. Based on the room-temperature out-of-plane and in-plane intercorrelated polarizat

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Boldman, Walker L., Cheng Zhang, Thomas Z. Ward, et al. "Programmable Electrofluidics for Ionic Liquid Based Neuromorphic Platform." Micromachines 10, no. 7 (2019): 478. http://dx.doi.org/10.3390/mi10070478.

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Due to the limit in computing power arising from the Von Neumann bottleneck, computational devices are being developed that mimic neuro-biological processing in the brain by correlating the device characteristics with the synaptic weight of neurons. This platform combines ionic liquid gating and electrowetting for programmable placement/connectivity of the ionic liquid. In this platform, both short-term potentiation (STP) and long-term potentiation (LTP) are realized via electrostatic and electrochemical doping of the amorphous indium gallium zinc oxide (aIGZO), respectively, and pulsed bias m

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Tang, 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 (2018): 1184–88. http://dx.doi.org/10.1109/lgrs.2018.2834522.

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Dissertations / Theses on the topic "Neuromorphic platform"

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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.

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Ford, 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.

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URGESE, GIANVITO. "Computational Methods for Bioinformatics Analysis and Neuromorphic Computing." Doctoral thesis, Politecnico di Torino, 2016. http://hdl.handle.net/11583/2646486.

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The latest biological discoveries and the exponential growth of more and more sophisticated biotechnologies led in the current century to a revolution that totally reshaped the concept of genetic study. This revolution, which began in the last decades, is still continuing thanks to the introduction of new technologies capable of producing a huge amount of biological data in a relatively short time and at a very low price with respect to some decades ago. These new technologies are known as Next Generation Sequencing (NGS). These platforms perform massively parallel sequencing of both RNA and D

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Wu, Jiaming. "A modular dynamic Neuro-Synaptic platform for Spiking Neural Networks." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASP145.

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Que le réseau de neurones soit biologique ou artificiel, il possède une unité de calcul fondamentale : le neurone. Ces neurones, interconnectés par des synapses, forment ainsi des réseaux complexes qui permettent d’obtenir une pluralité de fonctions. De même, le réseau de neurones neuromorphique, ou plus généralement les ordinateurs neuromorphiques, nécessitent également ces deux éléments fondamentaux que sont les neurones et les synapses. Dans ce travail, nous introduisons une unité matérielle neuro-synaptique à impulsions, inspirée de la biologie et entièrement réalisée avec des composants é

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Nease, Stephen H. "Neural and analog computation on reconfigurable mixed-signal platforms." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53999.

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This work addresses neural and analog computation on reconfigurable mixed-signal platforms. Many engineered systems could gain tremendous benefits by emulating neural systems. For example, neural systems are incredibly power efficient and fault-tolerant. They are also capable of types of computation that we cannot yet match with conventional computers. Neuromorphic engineers typically implement neural computation using analog circuits because they are low-power and naturally model some aspects of neurobiology. One problem with analog circuits is that they are typically inflexible. To address t

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SECCO, JACOPO. "Memristor Platforms for Pattern Recognition Memristor Theory, Systems and Applications." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2680573.

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In the last decade a large scientific community has focused on the study of the memristor. The memristor is thought to be by many the best alternative to CMOS technology, which is gradually showing its flaws. Transistor technology has developed fast both under a research and an industrial point of view, reducing the size of its elements to the nano-scale. It has been possible to generate more and more complex machinery and to communicate with that same machinery thanks to the development of programming languages based on combinations of boolean operands. Alas as shown by Moore’s law, th

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Mohamed, Abdalla Mohab Sameh. "Reservoir computing in lithium niobate on insulator platforms." Electronic Thesis or Diss., Ecully, Ecole centrale de Lyon, 2024. http://www.theses.fr/2024ECDL0051.

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Cette étude concerne le calcul par réservoir à retard temporel, en anglais Time-Delay Reservoir Computing (TDRC) dans les plateformes de photonique intégré, en particulier la plateforme Lithium Niobate On Insulator (LNOI). Nous proposons une nouvelle architecture intégrée « tout optique », avec seulement un déphaseur comme paramètre modifiable pouvant atteindre de bonnes performances sur plusieurs tâches de référence de calcul par réservoir. Nous étudions également l'espace de conception de cette architecture et le fonctionnement asynchrone du TDRC, qui s'écarte du cadre plus courant consistan

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Farahini, 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.

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Wei-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.

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碩士<br>國立成功大學<br>微電子工程研究所<br>107<br>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 hardw

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Book chapters on the topic "Neuromorphic platform"

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Varshika, M. L., and Anup Das. "Platform-Based Design of Embedded Neuromorphic Systems." In Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19568-6_12.

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Chung, Daesu, Reid Hirata, T. Nathan Mundhenk, et al. "A New Robotics Platform for Neuromorphic Vision: Beobots." In Biologically Motivated Computer Vision. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36181-2_56.

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Sugiarto, Indar, Agustinus Bimo Gumelar, and Astri Yogatama. "Embedded Machine Learning on a Programmable Neuromorphic Platform." In Lecture Notes in Electrical Engineering. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9781-4_13.

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Safa, Ali, Lars Keuninckx, Georges Gielen, and Francky Catthoor. "Design of a Drone Platform for Sensor Fusion Data Acquisition." In Neuromorphic Solutions for Sensor Fusion and Continual Learning Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63565-6_3.

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Cheng, Jingde. "Can “Neuromorphic Completeness” and “Brain-Inspired Computing” Provide a Promising Platform for Artificial General Intelligence?" In Advances in Intelligent Automation and Soft Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81007-8_14.

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Shahsavari, Mahyar, Philippe Devienne, and Pierre Boulet. "Spiking Neural Computing in Memristive Neuromorphic Platforms." In Handbook of Memristor Networks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-76375-0_25.

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Kasabov, Nikola K. "From von Neumann Machines to Neuromorphic Platforms." In Springer Series on Bio- and Neurosystems. Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-57715-8_20.

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Li, 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. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8135-9_20.

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Narduzzi, Simon, Dorvan Favre, Nuria Pazos Escudero, and L. Andrea Dunbar. "Deploying a Convolutional Neural Network on Edge MCU and Neuromorphic Hardware Platforms." In Industrial Artificial Intelligence Technologies and Applications. River Publishers, 2023. http://dx.doi.org/10.1201/9781003377382-10.

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Barchi, 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. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23425-6_9.

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Conference papers on the topic "Neuromorphic platform"

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Béna, Gabriel, Timo Wunderlich, Mahmoud Akl, Bernhard Vogginger, Christian Mayr, and Hector A. Gonzalez. "Event-based backpropagation on the neuromorphic platform SpiNNaker2." In 2025 Neuro Inspired Computational Elements (NICE). IEEE, 2025. https://doi.org/10.1109/nice65350.2025.11065716.

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Huang, Jiaxin, Bernhard Vogginger, Florian Kelber, Hector Gonzalez, Klaus Knobloch, and Christian Georg Mayr. "Fast Switching Serial and Parallel Paradigms of SNN Inference on Multi-Core Heterogeneous Neuromorphic Platform SpiNNaker2." In 2024 International Conference on Neuromorphic Systems (ICONS). IEEE, 2024. https://doi.org/10.1109/icons62911.2024.00025.

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Azevedo, J., A. Das, B. Jacob, et al. "III-V semiconductor nanowires and nanopillar arrays for an Insect Vision Inspired Neuromorphic On-Chip Platform." In 2024 IEEE Photonics Conference (IPC). IEEE, 2024. https://doi.org/10.1109/ipc60965.2024.10799627.

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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.

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Buckley, 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. OSA, 2017. http://dx.doi.org/10.1364/cleo_si.2017.sth1n.3.

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Haessig, 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.

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Sugiarto, 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.

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Dean, Mark E., Jason Chan, Christopher Daffron, et al. "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.

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Bui 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.

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El 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. FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2023. http://dx.doi.org/10.29363/nanoge.neumatdecas.2023.074.

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Reports on the topic "Neuromorphic platform"

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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), 2019. http://dx.doi.org/10.2172/1761866.

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