Results 11 to 20 of about 13,642,405 (235)
Two-Dimensional Oscillatory Neural Networks for Energy Efficient Neuromorphic Computing
, 2020 Neuro-inspired computing architectures are one of the leading candidates to solve complex and large-scale associative learning problems for AI applications. The two key building blocks for neuromorphic computing are the neuron and the synapse, which form
Linares-Barranco, Bernabé +19 more
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Advanced Science, 2021 Organic neuromorphic computing/sensing platforms are a promising concept for local monitoring and processing of biological signals in real time. Neuromorphic devices and sensors with low conductance for low power consumption and high conductance for low ...
Sol‐Kyu Lee +7 more
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Algorithm/Architecture Co-Design for Low-Power Neuromorphic Computing [PDF]
The development of computing systems based on the conventional von Neumann architecture has slowed down in the past decade as complementary metal-oxide-semiconductor (CMOS) technology scaling becomes more and more difficult.
Zheng, Nan
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Optical synaptic devices with ultra-low power consumption for neuromorphic computing
Light: Science & Applications, 2022 AbstractBrain-inspired neuromorphic computing, featured by parallel computing, is considered as one of the most energy-efficient and time-saving architectures for massive data computing. However, photonic synapse, one of the key components, is still suffering high power consumption, potentially limiting its applications in artificial neural system.
Chenguang Zhu +11 more
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Nonvolatile Memories in Spiking Neural Network Architectures: Current and Emerging Trends [PDF]
, 2022 A sustainable computing scenario demands more energy-efficient processors. Neuromorphic systems mimic biological functions by employing spiking neural networks for achieving brain-like efficiency, speed, adaptability, and intelligence.
Corradi, Federico; id_orcid +6 more
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Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network
Sensors, 2023 Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture.
Hyunho Seok +4 more
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Neuromorphic computing for content-based image retrieval.
PLoS ONE, 2022 Neuromorphic computing mimics the neural activity of the brain through emulating spiking neural networks. In numerous machine learning tasks, neuromorphic chips are expected to provide superior solutions in terms of cost and power efficiency.
Te-Yuan Liu +3 more
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A Low-Power Domino Logic Architecture for Memristor-Based Neuromorphic Computing [PDF]
Proceedings of the International Conference on Neuromorphic Systems, 2019 We propose a domino logic architecture for memristor-based neuromorphic computing. The design uses the delay of memristor RC circuits to represent synaptic computations and a simple binary neuron activation function. Synchronization schemes are proposed for communicating information between neural network layers, and a simple linear power model is ...
Cory E. Merkel, Animesh Nikam
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Systematic configuration and automatic tuning of neuromorphic systems [PDF]
, 2011 In the past recent years several research groups have proposed neuromorphic Very Large Scale Integration (VLSI) devices that implement event-based sensors or biophysically realistic networks of spiking neurons.
Sheik, S. +14 more
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A VLSI neuromorphic device for implementing spike-based neural networks [PDF]
, 2011 Indiveri G, Chicca E. A VLSI neuromorphic device for implementing spike-based neural networks. Presented at the Proceedings of the 21st Italian Workshop on Neural Nets (WIRN).We present a neuromorphic VLSI device which comprises hybrid analog/digital ...
Morabito, C. F. +5 more
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