Relevant bibliographies by topics / Neuromorphic platform

Academic literature on the topic 'Neuromorphic platform'

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Published: 14 March 2023

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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 (November 14, 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 implementation of a parallel DNA sequence matching algorithm implemented by using the MPI programming paradigm ported to the SpiNNaker platform. In our implementation, all cores available in the board are configured for executing in parallel an optimised version of the Boyer-Moore (BM) algorithm. Exploiting this application, we benchmarked the SpiNNaker platform in terms of scalability and synchronisation latency. Experimental results indicate that the SpiNNaker parallel architecture allows a linear performance increase with the number of used cores and shows better scalability compared to a general-purpose multi-core computing platform.
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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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Russo, 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.

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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 Sensor (DVS128), SpiNNaker board and a servo motor, creating a platform for a neuromorphic robotic system controlled by a Spiking Neural Network, which is demonstrated on the task of intercepting incoming objects. The data rate of the implemented interface board is 24.64 k symbols/s and the latency for generating commands is about 11ms. The complete system is run only by batteries, making it very suitable for robotic applications.
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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 models, coding methods, learning algorithms, and neuromorphic computing platforms, and according to these to propose some expected development, such as improving the balance between bio-mimicry and cost of computing for neuron models, compounding coding methods, unsupervised learning algorithms in SNN, and digital-analog computing platform.
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Zhai, 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.

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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 polarization effect of 2D α-In2Se3, we designed a reconfigurable hardware platform, which can switch from continuously modulated conductance for emulating synapse to spiking behavior for mimicking neuron. More crucially, we demonstrate the application of such proof-of-concept reconfigurable 2D ferroelectric devices on a spiking neural network with an accuracy of 95.8% and self-adaptive grow-when required network with an accuracy of 85% by dynamically shrinking its nodes by 72%, which exhibits more powerful learning ability and efficiency than the static neural network.
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Boldman, 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.

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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 measurements are demonstrated for lower power considerations. While compatible with resistive elements, we demonstrate a platform based on transitive amorphous indium gallium zinc oxide (aIGZO) pixel elements. Using a lithium based ionic liquid, we demonstrate both potentiation (decrease in device resistance) and depression (increase in device resistance), and propose a 2D platform array that would enable a much higher pixel count via Active Matrix electrowetting.
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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 (August 2018): 1184–88. http://dx.doi.org/10.1109/lgrs.2018.2834522.

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Bose, 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.

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Discontinuous metal films, comprising nanoscale gold islands, exhibit correlated avalanches of electrical signals that mimic those observed in the cortex, providing an interesting platform for brain-inspired computing.
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Sugiarto, 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.

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Deep learning (DL) has been considered as a breakthrough technique in the field of artificial intelligence and machine learning. Conceptually, it relies on a many-layer network that exhibits a hierarchically non-linear processing capability. Some DL architectures such as deep neural networks, deep belief networks and recurrent neural networks have been developed and applied to many fields with incredible results, even comparable to human intelligence. However, many researchers are still sceptical about its true capability: can the intelligence demonstrated by deep learning technique be applied for general tasks? This question motivates the emergence of another research discipline: neuromorphic computing (NC). In NC, researchers try to identify the most fundamental ingredients that construct intelligence behaviour produced by the brain itself. To achieve this, neuromorphic systems are developed to mimic the brain functionality down to cellular level. In this paper, a neuromorphic platform called SpiNNaker is described and evaluated in order to understand its potential use as a platform for a deep learning approach. This paper is a literature review that contains comparative study on algorithms that have been implemented in SpiNNaker.
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Petrov, 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.

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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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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 this shortcoming, our lab has developed reconfigurable analog systems known as Field Programmable Analog Arrays (FPAAs). This dissertation consists of two main parts. The first is the implementation of neural and analog circuits on FPAAs. We first implemented an adaptive winner-take-all circuit, which could model attention in neural systems. Next, we modeled the dendrite, which is the conductive tissue that relays inputs from synapses to the neuron cell body. We also implemented a subtractive music synthesizer, perhaps providing the electronic music synthesis community with a good platform for experimentation. Finally, we conducted a number of neural learning experiments on a neuromorphic platform. The second part of this dissertation includes design aspects of new FPAAs, including configurable blocks that can be used as current-mode DACs in a digitally-enhanced FPAA, the RASP 2.9v. We also consider the design of a new neuromorphic platform containing 256 neurons and over 200,000 synapses, many with learning capability. We also created an active delay line that could be used for beamforming or FIR filter applications. In summary, this work adds to the field of reconfigurable systems by both showing how to implement circuits with them and creating new systems based on lessons learned while working with previous systems.
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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
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.
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Book chapters on the topic "Neuromorphic platform"

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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.

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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, 119–28. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9781-4_13.

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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, 111–19. Cham: 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, 691–728. Cham: 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, 661–77. Berlin, Heidelberg: 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, 277–89. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8135-9_20.

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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, 167–86. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23425-6_9.

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Soltani 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.

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Zins, 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.

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Fear conditioning is a behavioral paradigm of learning to predict aversive events. It is a form of associative learning that memorizes an undesirable stimulus (e.g., an electrical shock) and a neutral stimulus (e.g., a tone), resulting in a fear response (such as running away) to the originally neutral stimulus. The association of concurrent events is implemented by strengthening the synaptic connection between the neurons. In this paper, with an analogous methodology, we reproduce the classic fear conditioning experiment of rats using mobile robots and a neuromorphic system. In our design, the acceleration from a vibration platform substitutes the undesirable stimulus in rats. Meanwhile, the brightness of light (dark vs. light) is used for a neutral stimulus, which is analogous to the neutral sound in fear conditioning experiments in rats. The brightness of the light is processed with sparse coding in the Intel Loihi chip. The simulation and experimental results demonstrate that our neuromorphic robot successfully, for the first time, reproduces the fear conditioning experiment of rats with a mobile robot. The work exhibits a potential online learning paradigm with no labeled data required. The mobile robot directly memorizes the events by interacting with its surroundings, essentially different from data-driven methods.
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"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.

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Conference papers on the topic "Neuromorphic platform"

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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. Washington, D.C.: 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, 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.

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

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Nease, 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.

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Galicia, 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.

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Park, 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.

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

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