Ternary In-Memory Computing with Cryogenic Quantum Anomalous Hall Effect Memories

Author:

Govindankutty Arun1ORCID,Alam Shamiul2ORCID,Das Sanjay1ORCID,Challapalle Nagadastagiri3ORCID,Aziz Ahmedullah2ORCID,George Sumitha1ORCID

Affiliation:

1. North Dakota State University, Fargo, ND, USA

2. University of Tennessee, Knoxville, TN, USA

3. NVIDIA, Santa Clara, CA, USA

Publisher

ACM

Reference14 articles.

1. Cryogenic Performance for Compute-in-Memory Based Deep Neural Network Accelerator

2. D. Min . et. al. Cryocache : A fast, large, and cost-effective cache architecture for cryogenic computing . In Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS '20 , page 449 -- 464 , New York, NY, USA , 2020 . Association for Computing Machinery. D.Min. et. al. Cryocache: A fast, large, and cost-effective cache architecture for cryogenic computing. In Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS '20, page 449--464, New York, NY, USA, 2020. Association for Computing Machinery.

3. Energy-Efficient Superconducting Computing—Power Budgets and Requirements

4. MANA: A Monolithic Adiabatic iNtegration Architecture Microprocessor Using 1.4-zJ/op Unshunted Superconductor Josephson Junction Devices

5. F. Wang et. al. Dram retention at cryogenic temperatures. In 2018 IEEE Interna- tional Memory Workshop (IMW) , pages 1 -- 4 , 2018 . F. Wang et. al. Dram retention at cryogenic temperatures. In 2018 IEEE Interna- tional Memory Workshop (IMW), pages 1--4, 2018.

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