Results 11 to 20 of about 14,142 (285)

A Review of Nanowire Devices Applied in Simulating Neuromorphic Computing [PDF]

Nanomaterials
With the rapid advancement of artificial intelligence and machine learning technologies, the demand for enhanced device computing capabilities has significantly increased.
Tianci Huang, Yuxuan Wang, Zhihan Jin, Hao Liu, Kaili Wang, Tan Leong Chee, Yi Shi, Shancheng Yan   +7 more
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Recent Progress in Neuromorphic Computing from Memristive Devices to Neuromorphic Chips

Advanced Devices & Instrumentation
Neuromorphic computing, drawing inspiration from the brain, stands out for its high energy efficiency in executing complex tasks. Memristive device-based neuromorphic computing has demonstrated ultrahigh efficiency. While there are numerous review papers
Yike Xiao, Cheng Gao, Juncheng Jin, Weiling Sun, Bowen Wang, Yukun Bao, Chen Liu, Wei Huang, Hui Zeng, Yefeng Yu   +9 more
doaj   +2 more sources

Transistor-Based Synaptic Devices for Neuromorphic Computing

Crystals
Currently, neuromorphic computing is regarded as the most efficient way to solve the von Neumann bottleneck. Transistor-based devices have been considered suitable for emulating synaptic functions in neuromorphic computing due to their synergistic ...
Wen Huang, Huixing Zhang, Zhengjian Lin, Pengjie Hang, Xing’ao Li   +4 more
doaj   +2 more sources

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, Alice Mizrahi, Damien Querlioz, Julie Grollier   +3 more
openaire   +2 more sources

Is Neuromorphic MNIST Neuromorphic? Analyzing the Discriminative Power of Neuromorphic Datasets in the Time Domain

Frontiers in Neuroscience, 2021
A major characteristic of spiking neural networks (SNNs) over conventional artificial neural networks (ANNs) is their ability to spike, enabling them to use spike timing for coding and efficient computing.
Laxmi R. Iyer, Yansong Chua, Haizhou Li, Haizhou Li   +3 more
doaj   +1 more source

Mechanical Properties Analysis of Flexible Memristors for Neuromorphic Computing [PDF]

Nano-Micro Letters
Tianyu Wang, Jialin Meng, Meng Jialin
exaly   +2 more sources

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, Sumit Bam Shrestha, Daniel Ben Dayan Rubin, Adam Kupryjanow, Garrick Orchard, Lukasz Pindor, Timothy Shea, Mike Davies   +7 more
doaj   +1 more source

Simulation-based inference for model parameterization on analog neuromorphic hardware

Neuromorphic Computing and Engineering, 2023
The BrainScaleS-2 (BSS-2) system implements physical models of neurons as well as synapses and aims for an energy-efficient and fast emulation of biological neurons.
Jakob Kaiser, Raphael Stock, Eric Müller, Johannes Schemmel, Sebastian Schmitt   +4 more
doaj   +1 more source

Neuromorphic quantum computing

Physical Review E, 2022
9 pages, 8 ...
Pehle, Christian, Wetterich, Christof
openaire   +3 more sources

Recent Progress of Optoelectronic and All‐Optical Neuromorphic Devices: A Comprehensive Review of Device Structures, Materials, and Applications

Advanced Intelligent Systems, 2021
Conventional von Neumann–based computing systems have inherent limitations such as high hardware complexity, relatively inferior energy efficiency, and low bandwidth.
Seungho Song, Jeehoon Kim, Sung Min Kwon, Jeong‐Wan Jo, Sung Kyu Park, Yong‐Hoon Kim   +5 more
doaj   +1 more source