Results 21 to 30 of about 31,264 (300)
Physics for neuromorphic computing [PDF]
Nature Reviews Physics, 2020 Neuromorphic computing takes inspiration from the brain to create energy efficient hardware for information processing, capable of highly sophisticated tasks. In this article, we make the case that building this new hardware necessitates reinventing electronics. We show that research in physics and material science will be key to create artificial nano-
Danijela Marković +3 more
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A triple-level cell charge trap flash memory device with CVD-grown MoS2
Results in Physics, 2022 This study investigates the triple-level cell (TLC) memory retention of a MoS2-channel based charge trap flash (CTF) device. A top-gated CTF device with a high-κ gate dielectric is found to have a high coupling ratio, which enhances the tunneling ...
Minkyung Kim +9 more
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An Optimized Deep Spiking Neural Network Architecture Without Gradients
IEEE Access, 2022 We present an end-to-end trainable modular event-driven neural architecture that uses local synaptic and threshold adaptation rules to perform transformations between arbitrary spatio-temporal spike patterns.
Yeshwanth Bethi +4 more
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Event-Based Feature Extraction Using Adaptive Selection Thresholds
Sensors, 2020 Unsupervised feature extraction algorithms form one of the most important building blocks in machine learning systems. These algorithms are often adapted to the event-based domain to perform online learning in neuromorphic hardware. However, not designed
Saeed Afshar +5 more
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The Intel neuromorphic DNS challenge
Neuromorphic Computing and Engineering, 2023 A critical enabler for progress in neuromorphic computing research is the ability to transparently evaluate different neuromorphic solutions on important tasks and to compare them to state-of-the-art conventional solutions.
Jonathan Timcheck +7 more
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CoRR, 2020 Neuromorphic engineering is a rapidly developing field that aims to take inspiration from the biological organization of neural systems to develop novel technology for computing, sensing, and actuating. The unique properties of such systems call for new signal processing and control paradigms.
Luka Ribar, Rodolphe Sepulchre
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IEEE Access, 2022 Neuromorphic hardware is a system with massive potential to enable efficient computing by mimicking the human brain. The novel system processes information using neuron spikes (Action Potentials) and the synaptic connections between neurons are trained ...
Suman Hu +11 more
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Crystals, 2021 A synaptic device that contains weight information between two neurons is one of the essential components in a neuromorphic system, which needs highly linear and symmetric characteristics of weight update.
Minkyung Kim +10 more
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Frontiers in Neuromorphic Engineering [PDF]
Frontiers in Neuroscience, 2011 ISSN:1662 ...
Indiveri, G, Horiuchi, T K
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Toward Near-Real-Time Training With Semi-Random Deep Neural Networks and Tensor-Train Decomposition
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 In recent years, deep neural networks have shown to achieve state-of-the-art performance on several classification and prediction tasks. However, these networks demand undesirable lengthy training times coupled with high computational resources (memory ...
Humza Syed +3 more
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