On-Device Continual Learning With STT-Assisted-SOT MRAM-Based In-Memory Computing-Reference-Cited by-同舟云学术

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On-Device Continual Learning With STT-Assisted-SOT MRAM-Based In-Memory Computing

Published:2024-08 Issue:8 Volume:43 Page:2393-2404
ISSN:0278-0070
Container-title:IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
language:
Short-container-title:IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

Author:

Zhang Fan¹^ORCID,Sridharan Amitesh¹,Hwang William²^ORCID,Xue Fen²,Tsai Wilman³^ORCID,Wang Shan Xiang⁴^ORCID,Fan Deliang¹

Affiliation:

1. Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA

2. Department of Electrical Engineering, Stanford University, Stanford, CA, USA

3. Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA

4. Department of Electrical Engineering and the Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA

Funder

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

https://ieeexplore.ieee.org/ielam/43/10604458/10453336-aam.pdf

Reference63 articles.

1. A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond

2. A Survey of ReRAM-Based Architectures for Processing-In-Memory and Neural Networks

3. Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks

4. Energy Efficient In-Memory Binary Deep Neural Network Accelerator with Dual-Mode SOT-MRAM

5. A 1.23-GHz 16-kb Programmable and Generic Processing-in-SRAM Accelerator in 65nm

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