Effect of layer-specific synaptic retention characteristics on the accuracy of deep neural networks-Reference-Cited by-同舟云学术

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Effect of layer-specific synaptic retention characteristics on the accuracy of deep neural networks

Published:2023-02 Issue: Volume:200 Page:108570
ISSN:0038-1101
Container-title:Solid-State Electronics
language:en
Short-container-title:Solid-State Electronics

Author:

Yoo Ho-Nam,Park Min-Kyu,Park Byung-Gook,Lee Jong-Ho

Publisher

Elsevier BV

Subject

Materials Chemistry,Electrical and Electronic Engineering,Condensed Matter Physics,Electronic, Optical and Magnetic Materials

Reference24 articles.

1. Deep residual learning for image recognition;He,2016

2. Emerging memory technologies for neuromorphic computing;Kim;Nanotechnology,2018

3. Si-based FET-type synaptic device with short-term and long-term plasticity using high-κ gate-stack;Seo;IEEE Trans Electron Devices,2019

4. Field effect transistor-type devices using high-κ gate insulator stacks for neuromorphic applications;Park;ACS Appl Electron Mater,2019

5. Reliability perspective on neuromorphic computing based on analog RRAM;Wu,2019

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1. Modified Discrete Neural Network PID Controller for Controlling the Helicopters Turboshaft Engines Free Turbine Speed;2023 IEEE 5th International Conference on Modern Electrical and Energy System (MEES);2023-09-27

2. Helicopters Turboshaft Engines Parameters Identification Using Neural Network Technologies Based on the Kalman Filter;Information and Communication Technologies in Education, Research, and Industrial Applications;2023

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