Results 21 to 30 of about 2,883,478 (184)
Biologically-Inspired Neuromorphic Computing [PDF]
Science Progress, 2019 Advances in integrated circuitry from the 1950s to the present day have enabled a revolution in technology across the world. However, fundamental limits of circuitry make further improvements through historically successful methods increasingly challenging.
Wilkie Olin-Ammentorp, Nathaniel Cady
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Magnetic Elements for Neuromorphic Computing [PDF]
Molecules, 2020 Neuromorphic computing is assumed to be significantly more energy efficient than, and at the same time expected to outperform, conventional computers in several applications, such as data classification, since it overcomes the so-called von Neumann bottleneck.
Tomasz Blachowicz, Andrea Ehrmann
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Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective [PDF]
, 2019 On metrics of density and power efficiency, neuromorphic technologies have the potential to surpass mainstream computing technologies in tasks where real-time functionality, adaptability, and autonomy are essential.
Abbott +56 more
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Polaritonic Neuromorphic Computing Outperforms Linear Classifiers [PDF]
Nano Letters, 2020 Machine learning software applications are nowadays ubiquitous in many fields of science and society for their outstanding capability of solving computationally vast problems like the recognition of patterns and regularities in big datasets. One of the main goals of research is the realization of a physical neural network able to perform data ...
Ballarini Dario +12 more
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2022 roadmap on neuromorphic computing and engineering [PDF]
Neuromorph. Comput. Eng., 2021 Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer
D. Christensen +58 more
semanticscholar +1 more source
Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition [PDF]
, 2015 A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density.
Saxena, Vishal, Wu, Xinyu, Zhu, Kehan
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Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook
Proceedings of the IEEE, 2021 Deep artificial neural networks apply principles of the brain’s information processing that led to breakthroughs in machine learning spanning many problem domains. Neuromorphic computing aims to take this a step further to chips more directly inspired by
Mike Davies +7 more
semanticscholar +1 more source
Principled neuromorphic reservoir computing [PDF]
Nature CommunicationsAbstract Reservoir computing advances the intriguing idea that a nonlinear recurrent neural circuit—the reservoir—can encode spatio-temporal input signals to enable efficient ways to perform tasks like classification or regression. However, recently the idea of a monolithic reservoir network that simultaneously buffers input signals and ...
Denis Kleyko +5 more
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Six networks on a universal neuromorphic computing substrate [PDF]
, 2013 In this study, we present a highly configurable neuromorphic computing substrate and use it for emulating several types of neural networks. At the heart of this system lies a mixed-signal chip, with analog implementations of neurons and synapses and ...
Andreas eGrübl +10 more
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Large memcapacitance and memristance at Nb:SrTiO$_{3}$ / La$_{0.5}$Sr$_{0.5}$Mn$_{0.5}$Co$_{0.5}$O$_{3-\delta}$ Topotactic Redox Interface [PDF]
, 2020 The possibility to develop neuromorphic computing devices able to mimic the extraordinary data processing capabilities of biological systems spurs the research on memristive systems.
Acevedo, W. R. +9 more
core +2 more sources