Boosting RRAM-Based Mixed-Signal Accelerators in FD-SOI Technology for ML Applications-Reference-Cited by-同舟云学术

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Boosting RRAM-Based Mixed-Signal Accelerators in FD-SOI Technology for ML Applications

Published:2023-12 Issue:2 Volume:9 Page:159-167
ISSN:2329-9231
Container-title:IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
language:
Short-container-title:IEEE J. Explor. Solid-State Comput. Devices Circuits

Author:

Boni Andrea¹^ORCID,Malena Francesco¹,Saccani Francesco¹^ORCID,Amoretti Michele¹^ORCID,Caselli Michele¹^ORCID

Affiliation:

1. Department of Engineering and Architecture, University of Parma, Parma, Italy

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Electronic, Optical and Magnetic Materials

Link

http://xplorestaging.ieee.org/ielx7/6570653/10345396/10233848.pdf?arnumber=10233848

Reference25 articles.

1. Compute-in-Memory Chips for Deep Learning: Recent Trends and Prospects

2. Memory Devices and A/D Interfaces: Design Tradeoffs in Mixed-Signal Accelerators for Machine Learning Applications

3. CONV-SRAM: An Energy-Efficient SRAM With In-Memory Dot-Product Computation for Low-Power Convolutional Neural Networks

4. Experimental Investigation of 4-kb RRAM Arrays Programming Conditions Suitable for TCAM

5. RRAM for Compute-in-Memory: From Inference to Training

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Write System for Compact RRAM Memory Arrays Based on F-1T1R;2024 IEEE International Symposium on Circuits and Systems (ISCAS);2024-05-19

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