Results 231 to 240 of about 2,687,799 (289)

Noise‐Limited Bit Precision in Ferroelectric Synaptic Transistors for High‐Resolution Neuromorphic Computing

Advanced Functional Materials, EarlyView.
Low‐frequency noise spectroscopy defines the resolvable conductance states of synaptic FeFETs by coupling read‐current fluctuation with usable dynamic range. The resulting noise‐limited bit precision establishes a universal, device‐agnostic reliability metric beyond the memory window, enabling quantitative benchmarking and rational design of high ...
Jaehong Park, Yeonseop Shin, Changhyeon Han, Sangwoo Kim, Eunchan Park, Youngchan Cho, Jaehong Min, Hyunjun Ahn, Jaekyoung Park, Cheol‐Woong Yang, Jangsaeng Kim, Daewoong Kwon, Wonjun Shin   +12 more
wiley   +1 more source

Efficient GPT-4V level multimodal large language model for deployment on edge devices. [PDF]

Nat Commun
Yao Y, Yu T, Zhang A, Wang C, Cui J, Zhu H, Cai T, Chen C, Li H, Zhao W, He Z, Chen Q, Zhou R, Zou Z, Zhang H, Hu S, Zheng Z, Zhou J, Cai J, Han X, Zeng G, Li D, Liu Z, Sun M.   +23 more
europepmc   +1 more source

Light‐Induced Field‐Tunneling Synapses in Solution‐Processed Van Der Waals Heterostructures for Scalable, Retina‐Inspired Optical Sensing

Advanced Functional Materials, EarlyView.
A scalable, solution‐processed WSe2/ZrO2‐x van der Waals heterostructure realizes a light‐induced field‐tunneling synapse (LIFTS) that activates exclusively under bright illumination, emulating the photopic adaptation of the human retina at the device level.
Kijeong Nam, Jihyun Kim, Soobeen Ji, In Soo Kim, Sejin Kim, Eungseon Yeon, Do Kyung Hwang, Iva Plutnarová, Zdeněk Sofer, Sang Min Won, Joohoon Kang   +10 more
wiley   +1 more source

Real-time and resource-efficient banana bunch detection and localization with YOLO-BRFB on edge devices. [PDF]

Front Plant Sci
Wang S, Wei L, Zhang D, Chen L, Huang W, Du D, Lin K, Zheng Z, Duan J.   +8 more
europepmc   +1 more source

Real-time open-vocabulary perception for mobile robots on edge devices: a systematic analysis of the accuracy-latency trade-off. [PDF]

Front Robot AI
Park J, Kim P, Ko D.
europepmc   +1 more source

FCMI-YOLO: An efficient deep learning-based algorithm for real-time fire detection on edge devices. [PDF]

PLoS One
Lu J, Zheng Y, Guan L, Lin B, Shi W, Zhang J, Wu Y.   +6 more
europepmc   +1 more source

Reliable ECG Anomaly Detection on Edge Devices for Internet of Medical Things Applications. [PDF]

Sensors (Basel)
Hizem M, Bousbia L, Ben Dhiab Y, Aoueileyine MO, Bouallegue R.   +4 more
europepmc   +1 more source

Efficient Limb Range of Motion Analysis from a Monocular Camera for Edge Devices. [PDF]

Sensors (Basel)
Yan X, Zhang L, Liu B, Qu G.
europepmc   +1 more source

PixelCraftSR: Efficient Super-Resolution with Multi-Agent Reinforcement for Edge Devices. [PDF]

Sensors (Basel)
Rasool MJA, Ahmed S, Sharif SMA, Sevara M, Whangbo TK.   +4 more
europepmc   +1 more source

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Middleware for Edge Devices in Mobile Edge Computing

2021 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), 2021
In mobile edge computing, edge devices collect data, and an edge server performs computational or data processing tasks that need real-time processing. Depending upon the requested task's complexity, an edge server executes it locally or remotely in the cloud. When an edge server needs to offload its computational tasks, there could be a sudden failure
Manish Pandey, Young-Woo Kwon
openaire   +1 more source