Results 1 to 10 of about 2,883,478 (184)

Versatile optoelectronic memristor based on wide-bandgap Ga2O3 for artificial synapses and neuromorphic computing

Light: Science & Applications
Optoelectronic memristors possess capabilities of data storage and mimicking human visual perception. They hold great promise in neuromorphic visual systems (NVs).
Dongsheng Cui, Mengjiao Pei, Zhenhua Lin, Hong Zhang, Mengyang Kang, Yifei Wang, Xiangxiang Gao, Jie Su, Jinshui Miao, Yun Li, Jincheng Zhang, Yue Hao, Jingjing Chang   +12 more
semanticscholar   +1 more source

Experimental demonstration of highly reliable dynamic memristor for artificial neuron and neuromorphic computing

Nature Communications, 2022
Neuromorphic computing, a computing paradigm inspired by the human brain, enables energy-efficient and fast artificial neural networks. To process information, neuromorphic computing directly mimics the operation of biological neurons in a human brain ...
See‐On Park, Hakcheon Jeong, Jongyong Park, Jongmin Bae, Shinhyun Choi   +4 more
semanticscholar   +1 more source

Security of neuromorphic computing [PDF]

Proceedings of the 35th International Conference on Computer-Aided Design, 2016
Neuromorphic architectures are widely used in many applications for advanced data processing, and often implements proprietary algorithms. In this work, we prevent an attacker with physical access from learning the proprietary algorithm implemented by the neuromorphic hardware.
Chaofei Yang, Beiye Liu, Hai Li, Yiran Chen, Wujie Wen, Mark Barnell, Qing Wu, Jeyavijayan Rajendran   +7 more
openaire   +1 more source

Memory and information processing in neuromorphic systems

, 2015
A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized.
Indiveri, Giacomo, Liu, Shih-Chii
core   +1 more source

Flexible Solution‐Processable Black‐Phosphorus‐Based Optoelectronic Memristive Synapses for Neuromorphic Computing and Artificial Visual Perception Applications

Advances in Materials, 2023
Being renowned for operating with visible‐light pulses and electrical signals, optoelectronic memristive synaptic devices have excellent potential for neuromorphic computing systems and artificial visual information processing.
Dayanand Kumar, Hanrui Li, Uttam Kumar Das, Abdul Momin Syed, Nazek El‐Atab   +4 more
semanticscholar   +1 more source

Beyond Memristors: Neuromorphic Computing Using Meminductors

Micromachines, 2023
Resistors with memory (memristors), inductors with memory (meminductors) and capacitors with memory (memcapacitors) play different roles in novel computing architectures. We found that a coil with a magnetic core is an inductor with memory (meminductor) in terms of its inductance L(q) being a function of charge q.
openaire   +4 more sources

Resonate and Fire Neuron with Fixed Magnetic Skyrmions

, 2018
In the brain, the membrane potential of many neurons oscillates in a subthreshold damped fashion and fire when excited by an input frequency that nearly equals their eigen frequency.
Chen X., Damien Querlioz, Datta S., Dhritiman Bhattacharya, Jayasimha Atulasimha, Md. Ali Azam, Yu S.   +6 more
core   +1 more source

Multi-level, forming and filament free, bulk switching trilayer RRAM for neuromorphic computing at the edge

Nature Communications
CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased ...
Jaeseoung Park, Ashwani Kumar, Yucheng Zhou, Sangheon Oh, Jeong-Hoon Kim, Yuhan Shi, Soumil Jain, Gopabandhu Hota, Erbin Qiu, Amelie L. Nagle, I. K. Schuller, Catherine D. Schuman, Gert Cauwenberghs, D. Kuzum   +13 more
semanticscholar   +1 more source

Layer-wise synapse optimization for implementing neural networks on general neuromorphic architectures

, 2017
Deep artificial neural networks (ANNs) can represent a wide range of complex functions. Implementing ANNs in Von Neumann computing systems, though, incurs a high energy cost due to the bottleneck created between CPU and memory.
Gupta, Jayesh K, Kochenderfer, Mykel, Mern, John   +2 more
core   +1 more source

Towards Accurate and High-Speed Spiking Neuromorphic Systems with Data Quantization-Aware Deep Networks

, 2019
Deep Neural Networks (DNNs) have gained immense success in cognitive applications and greatly pushed today's artificial intelligence forward. The biggest challenge in executing DNNs is their extremely data-extensive computations. The computing efficiency
Liu, C., Liu, Fuqiang
core   +1 more source