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Advanced technologies for brain-inspired computing

Fabien Clermidy, Rodolphe Heliot, Alexandre Valentian, Christian Gamrat, Olivier Bichler, Marc Duranton, Bilel Blehadj, Olivier Temam
2014 2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC)  
The very same devices can also be used for improving connectivity of Neural Networks as demonstrated by an application.  ...  This paper aims at presenting how new technologies can overcome classical implementation issues of Neural Networks.  ...  Figure 8 : 8 2D (a) and 3D (b) designs of a neural processor. The two layers of the neural network are mapped onto separate silicon layers.  ... 
doi:10.1109/aspdac.2014.6742951 dblp:conf/aspdac/ClermidyHVGBDBT14 fatcat:jdgkb64yibf2hcwhcacajrxmom

Stochastic and asynchronous spiking dynamic neural fields

Benoit Chappet de Vangel, Cesar Torres-Huitzil, Bernard Girau
2015 2015 International Joint Conference on Neural Networks (IJCNN)  
Stochastic computing was extensively studied for artificial neural networks (ANN) implementation with a good time/area trade-off on up-to-date FPGAs.  ...  The low hardware cost and the cellular design of this model make it easily scalable.  ...  for largescale spiking neural networks simulation.  ... 
doi:10.1109/ijcnn.2015.7280776 dblp:conf/ijcnn/VangelTG15 fatcat:cfurhjvbtnhp5fenmyv7dfcdkq

Guest Editors' Introduction: Stochastic Computing for Neuromorphic Applications

Ilia Polian, John P. Hayes, Vincent T. Lee, Weikang Qian
2021 IEEE design & test  
The article shows how SC enables low-cost, low-power, and errortolerant hardware implementation of neural networks suitable for edge computing.  ...  It covers SC primitives and the tradeoffs that occur when designing larger SC-based systems.  ...  Acknowledgments We are thankful to Florian Neugebauer of the University of Stuttgart for input on state of the art in the "Further SC NN designs" section and for help with picture material. The  ... 
doi:10.1109/mdat.2021.3080989 fatcat:tycdcvndf5cndfmlw5qiqiwe5u

Complementary Metal‐Oxide Semiconductor and Memristive Hardware for Neuromorphic Computing

Mostafa Rahimi Azghadi, Ying-Chen Chen, Jason K. Eshraghian, Jia Chen, Chih-Yang Lin, Amirali Amirsoleimani, Adnan Mehonic, Anthony J. Kenyon, Burt Fowler, Jack C. Lee, Yao-Feng Chang
2020 Advanced Intelligent Systems  
need for low-power and high-speed processing in everyday life and edge computing devices demands a shift to computers with elevated capabilities but with low power consumption.  ...  large amount of data is to be processed in a short time.  ...  The neuron figure used in Figure 1a is designed by brgfx/Freepik.  ... 
doi:10.1002/aisy.202070050 fatcat:zm6gabaaerf3bchiimlmmcujlu

CMOS and Memristive Hardware for Neuromorphic Computing

Mostafa Rahimi Azghadi, Ying-Chen Chen, Jason K. Eshraghian, Jia Chen, Chih-Yang Lin, Amirali Amirsoleimani, Adnan Mehonic, Anthony J Kenyon, Burt Fowler, Jack C. Lee, Yao-Feng Chang
2020 Advanced Intelligent Systems  
The neuron figure used in Figure 1a is designed by brgfx/Freepik.  ...  Saber Moradi and Professor Indiveri for designing the circuit in Figure 3a and the chip, where its architecture is shown in Figure 11a .  ...  neural networks.  ... 
doi:10.1002/aisy.201900189 fatcat:lrspxweqlfb6bmmqir6mwzvx44

Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends

M. Lakshmi Varshika, Federico Corradi, Anup Das
2022 Electronics  
Neuromorphic systems mimic biological functions by employing spiking neural networks for achieving brain-like efficiency, speed, adaptability, and intelligence.  ...  This review collects the most recent trends in exploiting the physical properties of nonvolatile memory technologies for implementing efficient in-memory and in-device computing with spike-based neuromorphic  ...  (A) Biological neuron. (B) Spiking neural network with spike-based learning implemented through NVMs. (C) Crossbar array for spiking neural network.  ... 
doi:10.3390/electronics11101610 fatcat:x4aqw2xk55g5tmdfqvygyxh5eu

Memristive Crossbar Arrays for Storage and Computing Applications

Huihan Li, Shaocong Wang, Xumeng Zhang, Wei Wang, Rui Yang, Zhong Sun, Wanxiang Feng, Peng Lin, Zhongrui Wang, Linfeng Sun, Yugui Yao
2021 Advanced Intelligent Systems  
Nanocrossbar memory array with CRS structures to avoid the sneak current. a) Top panel: ECM-based CRS device connected serially with a resistor.  ...  Bottom panel: ECM-based CRS device without the series resistor.  ...  stochastic CMOS LIF neurons and which experimentally implemented a spiking restricted Boltzmann machine for MNIST classification  ... 
doi:10.1002/aisy.202100017 fatcat:ue2xlvc2yvhsbitubd4prugxby

Functional Oxides for Photoneuromorphic Engineering: Toward a Solar Brain

Amador Pérez-Tomás
2019 Advanced Materials Interfaces  
These constraints are particularly relevant for thin-film photovoltaics where extremely large-scale, large volume fabrication requires low cost fabrication in order to be competitive with dominant Si-based  ...  Another key factor in the adoption of oxide semiconductors is that they are compatible with the strict manufacturing requirements of large-scale, large-volume, flexible, low cost and disposable/reusable  ...  array neural network based on STDP learning rules.  ... 
doi:10.1002/admi.201900471 fatcat:ypssywb5njbqnhdo76bqjxnoum

Mapping high-performance RNNs to in-memory neuromorphic chips [article]

Manu V Nair, Giacomo Indiveri
2019 arXiv   pre-print
In this paper, we propose a new adaptive spiking neuron model that can be abstracted as a low-pass filter.  ...  However, most of these architectures rely on spiking neural networks, which typically perform poorly compared to their non-spiking counterparts in terms of accuracy.  ...  Lif and simplified srm neurons encode signals into spikes via a form of asynchronous pulse sigma-delta modulation. IEEE transactions on neural networks and learning systems, 28(5):1192-1205.  ... 
arXiv:1905.10692v4 fatcat:u43hewgcgfdppkxqj2b7ranaa4

Conductive Bridge Random Access Memory (CBRAM): Challenges and Opportunities for Memory and Neuromorphic Computing Applications

Haider Abbas, Jiayi Li, Diing Shenp Ang
2022 Micromachines  
Due to a rapid increase in the amount of data, there is a huge demand for the development of new memory technologies as well as emerging computing systems for high-density memory storage and efficient  ...  The emulation of biological synapses and neurons using CBRAM devices fabricated with various switching materials and device engineering and material innovation approaches are examined in depth.  ...  (g) Neural network training accuracy for devices with and without a barrier layer. Reprinted from Ref. [80].  ... 
doi:10.3390/mi13050725 pmid:35630191 pmcid:PMC9143014 fatcat:zyhkz2fzxvaezhm76nwehpr4d4

A Survey of ReRAM-Based Architectures for Processing-In-Memory and Neural Networks

Sparsh Mittal
2018 Machine Learning and Knowledge Extraction  
), and especially neural network (NN)-based accelerators has grown significantly.  ...  In this paper, we present a survey of techniques for designing ReRAM-based PIM and NN architectures.  ...  To implement SNN using ReRAM, they also discussed an MCA working as a network synapse, an analog spiking neuron design and a mapping scheme for configuring ReRAM-based SNN.  ... 
doi:10.3390/make1010005 dblp:journals/make/Mittal19 fatcat:ti3ud2v6l5bffegfn3gzrm2lca

A Review of Resistive Switching Devices: Performance Improvement, Characterization, and Applications

Tuo Shi, Rui Wang, Zuheng Wu, Yize Sun, Junjie An, Qi Liu
2021 Small Structures  
Depending on the information representation method, neural networks can be simply classified into artificial neural networks (ANNs) and spiking neuron networks (SNNs).  ...  A hybrid SNN with CMOS LIF neurons and RS device-based synapses that implements a spike-timing-dependent plasticity (STDP) learning rule was demonstrated.  ...  His current research interest focuses on neuromorphic computing based on memristors, including materials, devices, and applications.  ... 
doi:10.1002/sstr.202000109 fatcat:cfxvdac2wraaxfocg7aybuakmy

2020 Index IEEE Transactions on Circuits and Systems II: Express Briefs Vol. 67

2020 IEEE Transactions on Circuits and Systems - II - Express Briefs  
250-254 Jalali, M., see Kari Dolatabadi, A., TCSII Oct. 2020 1740-1744 Jalili, M., see Fang, X., TCSII March 2020 511-515 Jana, B., Roy, A.S., Saha, G., and Banerjee, S., A Low-Error, Memory-Based  ...  Adaptive Filtering; TCSII Oct. 2020 2229-2233 Qian, J., Lu, M., and Huang, N., Radar and Communication Co-Existence Design Based on Mutual Information Optimization; TCSII Dec. 2020 3577-3581 Qian,  ...  ., +, TCSII Oct. 2020 2184-2188 Concept of LIF Neuron Circuit for Rate Coding in Spike Neural Networks.  ... 
doi:10.1109/tcsii.2020.3047305 fatcat:ifjzekeyczfrbp5b7wrzandm7e

2020 Index IEEE Transactions on Electron Devices Vol. 67

2020 IEEE Transactions on Electron Devices  
DNP-NMR: Design and Experimental Results; TED Jan. 2020 328-334 Jaysankar, M., see Gupta, A., TED Dec. 2020 5349-5354 Jazaeri, F., Shalchian, M., and Sallese, J., Transcapacitances in EPFL HEMT Model  ...  Interaction Structure for Backward Wave Generation; TED March 2020 1227-1233 Narayanan, V., see Liang, Y., 1297-1304 Nardo, A., see Minetto, A., TED Nov. 2020 4602-4605 Nardo, A., see De Santi, C.,  ...  Yao, K., +, TED Oct. 2020 4328-4334 Simulation-Based Ultralow Energy and High-Speed LIF Neuron Using Silicon Bipolar Impact Ionization MOSFET for Spiking Neural Networks.  ... 
doi:10.1109/ted.2021.3054448 fatcat:r4ertn5jordkfjjvorvss7n6ju

Abstracts of the 11th Annual Meeting of the Israel Society for Neuroscience

2002 Neural Plasticity  
Thus, directed convergence combined with parallel growth of dendrites shapes the synaptie map in cultured neuronal networks.  ...  This may se.rve as a mechanism for activity-dependent pre-.synaptie plasticity.  ...  The perceptual learning procedure described here was designed to train this network by efficiently stimulating these neuronal populations and effectively prtmoting their spatial interactions.  ... 
doi:10.1155/np.2002.65 pmid:12516548 pmcid:PMC2565399 fatcat:6o7uzh2mtfawdao4mzwue32m2y
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