Nanofiber Channel Organic Electrochemical Transistors for Low‐Power Neuromorphic Computing and Wide‐Bandwidth Sensing Platforms [PDF]
Advanced Science, 2021 Organic neuromorphic computing/sensing platforms are a promising concept for local monitoring and processing of biological signals in real time. Neuromorphic devices and sensors with low conductance for low power consumption and high conductance for low ...
Sol‐Kyu Lee +7 more
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Room-temperature valley transistors for low-power neuromorphic computing [PDF]
Nature Communications, 2022 Valleytronic devices employ the electronic valley degree of freedom to realize potential low-power electronic applications. Here, the authors utilize a topological semiconductor to engineer valley polarization transistors with long lifetimes and ...
Jiewei Chen +9 more
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Synaptic Plasticity Engineering for Neural Precision, Temporal Learning, and Scalable Neuromorphic Systems [PDF]
Nano-Micro LettersHighlights This review provides an in-depth discussion of computing-unit optimization through synaptic plasticity engineering, enabling precise weight modulation in spatial models and effective temporal information processing in dynamic neural networks ...
Zhengjun Liu +4 more
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Low‐Power Computing with Neuromorphic Engineering
Advanced Intelligent Systems, 2021 The increasing power consumption in the existing computation architecture presents grand challenges for the performance and reliability of very‐large‐scale integrated circuits. Inspired by the characteristics of the human brain for processing complicated
Dingbang Liu, Hao Yu, Yang Chai
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Adaptive Extreme Edge Computing for Wearable Devices
Frontiers in Neuroscience, 2021 Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital ...
Erika Covi +6 more
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CMOS-compatible neuromorphic devices for neuromorphic perception and computing: a review
International Journal of Extreme Manufacturing, 2023 Neuromorphic computing is a brain-inspired computing paradigm that aims to construct efficient, low-power, and adaptive computing systems by emulating the information processing mechanisms of biological neural systems.
Yixin Zhu +7 more
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Large memcapacitance and memristance at Nb:SrTiO$_{3}$ / La$_{0.5}$Sr$_{0.5}$Mn$_{0.5}$Co$_{0.5}$O$_{3-\delta}$ Topotactic Redox Interface [PDF]
, 2020 The possibility to develop neuromorphic computing devices able to mimic the extraordinary data processing capabilities of biological systems spurs the research on memristive systems.
Acevedo, W. R. +9 more
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Synapse-Mimetic Hardware-Implemented Resistive Random-Access Memory for Artificial Neural Network
Sensors, 2023 Memristors mimic synaptic functions in advanced electronics and image sensors, thereby enabling brain-inspired neuromorphic computing to overcome the limitations of the von Neumann architecture.
Hyunho Seok +4 more
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Neuromorphic computing for content-based image retrieval.
PLoS ONE, 2022 Neuromorphic computing mimics the neural activity of the brain through emulating spiking neural networks. In numerous machine learning tasks, neuromorphic chips are expected to provide superior solutions in terms of cost and power efficiency.
Te-Yuan Liu +3 more
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Essential Characteristics of Memristors for Neuromorphic Computing
Advanced Electronic Materials, 2023 The memristor is a resistive switch where its resistive state is programable based on the applied voltage or current. Memristive devices are thus capable of storing and computing information simultaneously, breaking the Von Neumann bottleneck.
Wenbin Chen +6 more
doaj +1 more source