(Invited) Electrochemically-Tunable and Low-Power 2D Synapses for Neuromorphic Computing
ECS Meeting Abstracts, 2019Inspired 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.
openaire +1 more source
ECRAM as Scalable Synaptic Cell for High-Speed, Low-Power Neuromorphic Computing
2018 IEEE International Electron Devices Meeting (IEDM), 2018We 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 +11 more
openaire +1 more source
Neuromorphic computing based on Analog ReRAM as low power solution for edge application
2019 IEEE 11th International Memory Workshop (IMW), 2019We 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 +10 more
openaire +1 more source
Advanced Ultra Low-Power Deep Learning Applications with Neuromorphic Computing
2023 IEEE High Performance Extreme Computing Conference (HPEC), 2023Mark Barnell +6 more
openaire +1 more source
Low-power Analog In-memory Computing Neuromorphic Circuits
2023Roland Müller +4 more
openaire +1 more source
Low Power HfOx/TaOx stacked memristors with nanocolumn electrode for neuromorphic computing
NanotechnologyAbstract Emulating biological synaptic behavior using the resistive random access memory (RRAM) is promising for neuromorphic applications. A stacked HfOx/TaOx RRAM model with nanocolumn electrode for low-power neuromorphic computing was constructed, and the finite element method was used to simulate the reset and set processes.
Fei Yang +4 more
openaire +1 more source
Mimicking Synaptic Behaviors with Junctionless Transistor for Low Power Neuromorphic Computing
2022 IEEE International Conference on Emerging Electronics (ICEE), 2022Md. Hasan Raza Ansari +2 more
openaire +1 more source
MESO Neuron: A Low-Power and Ultrafast Spin Neuron for Neuromorphic Computing
IEEE Magnetics Letters, 2022Junwei Zeng +6 more
openaire +1 more source
Plasma‐Engineered AlGaN/GaN Optoelectronic Synapses for Low‐Power Neuromorphic Computing
Advanced Optical MaterialsAbstract The 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 ...
Yuliang Zhang +7 more
openaire +1 more source