Results 141 to 150 of about 14,261 (179)

Biopolymer based artificial synapses enable linear conductance tuning and low-power for neuromorphic computing

Nanoscale, 2022
The mitigating effects of synaptic nonlinearity and low power through AgNO3 doping was achieved in the biomaterial based artificial synapse.
Ke Zhang, Qi Xue, Chao Zhou, Wanneng Mo, Chun-Chao Chen, Ming Li, Tao Hang   +6 more
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Spintronic devices for ultra-low power neuromorphic computation (Special session paper)

2016 IEEE International Symposium on Circuits and Systems (ISCAS), 2016
Emerging spin-transfer torque mechanisms in devices like vertical spin valves, lateral spin valves, domain wall motion based devices, spin-torque oscillators and spin-orbit torque based devices have opened up new possibilities of mimicking various neural and synaptic functionalities by the underlying device physics.
Abhronil Sengupta, Karthik Yogendra, Kaushik Roy   +2 more
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Ultrafast and Low-Power 2D Bi₂O₂Se Memristors for Neuromorphic Computing Applications

Nano Letters, 2023
Memristors that emulate synaptic plasticity are building blocks for opening a new era of energy-efficient neuromorphic computing architecture, which will overcome the limitation of the von Neumann bottleneck. Layered two-dimensional (2D) Bi2O2Se, as an emerging material for next-generation electronics, is of great significance in improving the ...
Zilong Dong, Qilin Hua, Jianguo Xi, Yuanhong Shi, Tianci Huang, Xinhuan Dai, Jianan Niu, Bingjun Wang, Zhong Lin Wang, Weiguo Hu   +9 more
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ECRAM as Scalable Synaptic Cell for High-Speed, Low-Power Neuromorphic Computing

2018 IEEE International Electron Devices Meeting (IEDM), 2018
We demonstrate a nonvolatile Electro-Chemical Random-Access Memory (ECRAM) based on lithium (Li) ion intercalation in tungsten oxide (WO 3 ) for high-speed, low-power neuromorphic computing. Symmetric and linear update on the channel conductance is achieved using gate current pulses, where up to 1000 discrete states with large dynamic range and good ...
Jianshi Tang, Douglas Bishop, Seyoung Kim, Matt Copel, Tayfun Gokmen, Teodor Todorov, SangHoon Shin, Ko-Tao Lee, Paul Solomon, Kevin Chan, Wilfried Haensch, John Rozen   +11 more
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Low-power Analog and Mixed-signal IC Design of Multiplexing Neural Encoder in Neuromorphic Computing

2021 22nd International Symposium on Quality Electronic Design (ISQED), 2021
The research on computing clusters comprising neuromorphic systems has drawn the interest of many researchers in the field. Neural encoding is a crucial component that determines how the information is conveyed through a train of spikes, greatly impacting the mode of operations’ and systems’ performance to a large extent. Numerous encoding schemes have
Honghao Zheng, Nima Mohammadi, Kangjun Bai, Yang Yi   +3 more
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(Invited) Electrochemically-Tunable and Low-Power 2D Synapses for Neuromorphic Computing

ECS Meeting Abstracts, 2019
Inspired by the human brain, which is better at complex tasks such as pattern recognition than even supercomputers with much better efficiency, neuromorphic computing has recently attracted much research attention. Biological neural networks employ analog changes in neural connections strength (i.e.
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Neuromorphic computing based on Analog ReRAM as low power solution for edge application

2019 IEEE 11th International Memory Workshop (IMW), 2019
We have developed neuromorphic computing based on Analog ReRAM, Resistive Analog Neuromorphic Device (RAND), as low power solution for edge application. We have proposed perceptron circuit which has resistive elements to store weights as analog resistance and binarizes output from each layer in order to realize large scale integration and keep high ...
Takumi Mikawa, Ryutaro Yasuhara, Koji Katayama, Kazuyuki Kouno, Takashi Ono, Reiji Mochida, Yuriko Hayata, Masayoshi Nakayama, Hitoshi Suwa, Yasushi Gohou, Toru Kakiage   +10 more
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Spin wave based synapse and neuron for ultra low power neuromorphic computation system

2016 IEEE International Symposium on Circuits and Systems (ISCAS), 2016
In this work, we have proposed that the neural synapses and neurons can be realized by utilizing spin waves (SWs) as information carrier. The SWs is excited by spin torque nano-oscillator (STNO), and detected with several different physical mechanisms: 1) tunneling magnetic-resistance 2) spin pumping and 3) inverse spin hall effect.
Lang Zeng, Deming Zhang, Youguang Zhang, Fanghui Gong, Tianqi Gao, Sa Tu, Haiming Yu, Weisheng Zhao   +7 more
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Plasma‐Engineered AlGaN/GaN Optoelectronic Synapses for Low‐Power Neuromorphic Computing

Advanced Optical Materials
AbstractThe development of hardware‐level synaptic systems is crucial for enabling the advancement of modern humanoid robotics and artificial intelligence systems to emulate biological vision. While optoelectronic synapses offer a promising solution, current implementations often require complex multi‐material integration and floating gate ...
Yuliang Zhang, Yunwei Huang, Chenyang Huang, Yizhong Yang, Zhenmin Li, Yan Wang, Linbao Luo, Chunyan Wu   +7 more
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Vacancy‐Induced Synaptic Behavior in 2D WS₂ Nanosheet–Based Memristor for Low‐Power Neuromorphic Computing

Small, 2019
AbstractMemristors with nonvolatile memory characteristics have been expected to open a new era for neuromorphic computing and digital logic. However, existing memristor devices based on oxygen vacancy or metal‐ion conductive filament mechanisms generally have large operating currents, which are difficult to meet low‐power consumption requirements ...
Xiaobing Yan, Qianlong Zhao, Andy Paul Chen, Jianhui Zhao, Zhenyu Zhou, Jingjuan Wang, Hong Wang, Lei Zhang, Xiaoyan Li, Zuoao Xiao, Kaiyang Wang, Cuiya Qin, Gong Wang, Yifei Pei, Hui Li, Deliang Ren, Jingsheng Chen, Qi Liu   +17 more
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