Monolithically Integrated Complementary Ferroelectric FET XNOR Synapse for the Binary Neural Network-Reference-Cited by-同舟云学术

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Monolithically Integrated Complementary Ferroelectric FET XNOR Synapse for the Binary Neural Network

Published:2024-01-04 Issue:2 Volume:16 Page:2467-2476
ISSN:1944-8244
Container-title:ACS Applied Materials & Interfaces
language:en
Short-container-title:ACS Appl. Mater. Interfaces

Author:

Hwang Junghyeon¹,Joh Hongrae¹,Kim Chaeheon¹,Ahn Jinho²,Jeon Sanghun¹^ORCID

Affiliation:

1. School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Korea

2. Division of Materials Science and Engineering, Hanyang University, 222, Wangsimni-ro, Seonhdong-gu, Seoul 04763, Korea

Funder

National Research Foundation of Korea

Ministry of Trade, Industry and Energy

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsami.3c13945

Reference39 articles.

1. Pak, M.; Kim, S. A Review of Deep Learning in Image Recognition. 2017 4th international conference on computer applications and information processing technology (CAIPT); IEEE, 2017; pp 1–3.

2. A graph placement methodology for fast chip design

3. How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics

4. Prediction of Synthesis of 2D Metal Carbides and Nitrides (MXenes) and Their Precursors with Positive and Unlabeled Machine Learning

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1. A Reconfigurable Ferroelectric Transistor as An Ultra‐Scaled Cell for Low‐Power In‐Memory Data Processing;Advanced Electronic Materials;2024-08-05

2. Utilizing Dual-Port FeFETs for Energy-Efficient Binary Neural Network Inference Accelerators;IEEE Transactions on Electron Devices;2024-07

3. Dual-port ferroelectric NAND flash memory for large memory window, QLC programmable and disturbance-free operations;Journal of Materials Chemistry C;2024

4. Interaxnn: A Reconfigurable and Approximate In-Memory Processing Accelerator for Ultra-Low-Power Binary Neural Network Inference in Intermittently Powered Systems;2024

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