In-Memory Computing Accelerators for Emerging Learning Paradigms-Reference-Cited by-同舟云学术

In-Memory Computing Accelerators for Emerging Learning Paradigms

Published:2023-01-16 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th Asia and South Pacific Design Automation Conference
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Author:

Reis Dayane¹,Laguna Ann Franchesca²,Niemier Michael³,Hu Xiaobo Sharon³

Affiliation:

1. University of South Florida

2. De La Salle University, Manilla, The Philippines

3. University of Notre Dame

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3566097.3568356

Reference37 articles.

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2. S. Bhatia Transformer networks of human conceptual knowledge . Psychological Review , 2021 . S. Bhatia et al. Transformer networks of human conceptual knowledge. Psychological Review, 2021.

3. A. Ranjan , : A Crossbar Based Architecture for Memory Augmented Neural Networks . In DAC , New York, NY, USA , 2019 . ACM. A. Ranjan, et al. X-MANN: A Crossbar Based Architecture for Memory Augmented Neural Networks. In DAC, New York, NY, USA, 2019. ACM.

4. In-Memory Processing Paradigm for Bitwise Logic Operations;Kang W.;STT-MRAM. TMAG,2017

5. Computing in memory with FeFETs

Cited by 3 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. AFeCAM: An Energy Efficient Analog 1FeFET Content Addressable Memory;Proceedings of the Great Lakes Symposium on VLSI 2024;2024-06-12

2. Invited Paper: Algorithm/Hardware Co-Design for Few-Shot Learning at the Edge;2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD);2023-10-28

3. A Reconfigurable FeFET Content Addressable Memory for Multi-State Hamming Distance;IEEE Transactions on Circuits and Systems I: Regular Papers;2023-06