Special Session - Non-Volatile Memories: Challenges and Opportunities for Embedded System Architectures with Focus on Machine Learning Applications-Reference-Cited by-同舟云学术

Special Session - Non-Volatile Memories: Challenges and Opportunities for Embedded System Architectures with Focus on Machine Learning Applications

Published:2023-09-17 Issue: Volume:15 Page:11-20
ISSN:
Container-title:Proceedings of the International Conference on Compilers, Architecture, and Synthesis for Embedded Systems
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Author:

Henkel Jorg¹^ORCID,Siddhu Lokesh¹^ORCID,Bauer Lars¹^ORCID,Teich Jurgen²^ORCID,Wildermann Stefan²^ORCID,Tahoori Mehdi¹^ORCID,Mayahinia Mahta¹^ORCID,Castrillon Jeronimo³^ORCID,Khan Asif Ali³^ORCID,Farzaneh Hamid³^ORCID,Lima Joao Paulo C. De³^ORCID,Chen Jian-Jia⁴^ORCID,Hakert Christian⁴^ORCID,Chen Kuan-Hsun⁵^ORCID,Yang Chia-Lin⁶^ORCID,Cheng Hsiang-Yun⁷^ORCID

Affiliation:

1. Karlsruhe Institute of Technology, Karlsruhe, Karlsruhe, Germany

2. Friedrich-Alexander-Universitat, Erlangen, Erlangen, Germany

3. TU Dresden, Dresden, Dresden, Germany

4. TU Dortmund University, Dortmund, Dortmund, Germany

5. University of Twente, Enschede, Enschede, Netherlands

6. National Taiwan University, Taipei, Taipei, Taiwan

7. Academia Sinica, Nanking, Taipei, Taiwan

Funder

German Research Foundation (Deutsche Forschungsgemeinschaft)

Ministry of Science and Technology of Taiwan

Macronix Inc., Taiwan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3607889.3609088

Reference70 articles.

1. J. Ambrosi, et al. 2018. Hardware-Software Co-Design for an Analog-Digital Accelerator for Machine Learning. In Int. Conf. on Rebooting Computing. 1--13.

2. A. Ankit et al. 2019. PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference. In Int. Conf. on Architectural Support for Programming Languages and Operating Systems (ASPLOS). 715--731.

3. Magnetic Racetrack Memory: From Physics to the Cusp of Applications Within a Decade

4. S. Buschjäger, et al. 2021. Margin-Maximization in Binarized Neural Networks for Optimizing Bit Error Tolerance. In Design, Automation & Test in Europe Conf. (DATE). 673--678.

5. S. Buschjager, et al. 2018. Realization of Random Forest for Real-Time Evaluation through Tree Framing. In Int. Conf. on Data Mining (ICDM). 19--28.