Results 211 to 220 of about 4,441 (253)

Iontronics of nanofluidic conical pores: learning phenomena using voltage pulses.

Nanoscale
Portillo S, Cervera J, Manzanares JA, Bisquert J, Mafe S, Ramirez P.   +5 more
europepmc   +1 more source

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CMOS Compatible Low Power Consumption Ferroelectric Synapse for Neuromorphic Computing

IEEE Electron Device Letters, 2023
Tian-Yu Wang, Jialin Meng, Qing-Qing Sun   +2 more
exaly   +2 more sources

An Asynchronous Soft Macro for Ultra-Low Power Communication in Neuromorphic Computing

2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2022
Asynchronous networks-on-chip (NoCs) playa fundamental role to materialize energy efficiency and scalability of spiking neural network-based neuromorphic systems. An unmistakable trend in this field consists of using bundled-data encoding for NoC design, showing promise in overall cost metrics while incorporating moderate timing constraints.
Bertozzi D., Bhardwaj K., Nowick S. M.
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Spin-Transfer Torque Magnetic neuron for low power neuromorphic computing

2015 International Joint Conference on Neural Networks (IJCNN), 2015
Neuromorphic computing attempts to emulate the remarkable efficiency of the human brain in vision, perception and cognition related tasks. Nanoscale devices that offer a direct mapping to the underlying neural computations have emerged as a promising candidate for such neuromorphic architectures.
Abhronil Sengupta, Kaushik Roy 0001
openaire   +1 more source

Ultra-Low power neuromorphic computing with spin-torque devices

2013 Third Berkeley Symposium on Energy Efficient Electronic Systems (E3S), 2013
Emerging spin transfer torque (ST) devices such as lateral spin valves and domain wall magnets may lead to ultra-low-voltage, current-mode, spin-torque switches that can offer attractive computing capabilities, beyond digital switches. This paper reviews our work on ST-based non-Boolean data-processing applications, like neural-networks, which involve ...
Mrigank Sharad, Deliang Fan, Karthik Yogendra, Kaushik Roy   +3 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 0001   +2 more
openaire   +1 more source

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 AgNO 3  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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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 0002   +3 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 0001   +7 more
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Design of Low-Power Systems Based on Neuromorphic Computing

Applied and Computational Engineering
With the rapid advancement of the Internet of Things and edge intelligent computing, the demand for low-power, high-energy-efficient computing systems has become increasingly urgent. The traditional von Neumann architecture suffers from poor energy efficiency in data-intensive tasks due to the 'memory wall' problem.
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