V10: Hardware-Assisted NPU Multi-tenancy for Improved Resource Utilization and Fairness-Reference-Cited by-同舟云学术

V10: Hardware-Assisted NPU Multi-tenancy for Improved Resource Utilization and Fairness

Published:2023-06-17 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 50th Annual International Symposium on Computer Architecture
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Short-container-title:

Author:

Xue Yuqi¹^ORCID,Liu Yiqi¹^ORCID,Nai Lifeng²^ORCID,Huang Jian¹^ORCID

Affiliation:

1. University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

2. Google, Mountain View, California, USA

Funder

NSF (National Science Foundation)

Publisher

ACM

Reference55 articles.

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2. 2020. Hardware Accelerated GPU Scheduling. https://devblogs.microsoft.com/directx/hardware-accelerated-gpu-scheduling/ 2020. Hardware Accelerated GPU Scheduling. https://devblogs.microsoft.com/directx/hardware-accelerated-gpu-scheduling/

3. 2020. Understanding 8 types of Cross-Validation: A Deep dive explanation of cross-validation and its types. https://towardsdatascience.com/understanding-8-types-of-cross-validation-80c935a4976d 2020. Understanding 8 types of Cross-Validation: A Deep dive explanation of cross-validation and its types. https://towardsdatascience.com/understanding-8-types-of-cross-validation-80c935a4976d

4. 2022. Profile your model with Cloud TPU tools. https://cloud.google.com/tpu/docs/profile-tpu-vm 2022. Profile your model with Cloud TPU tools. https://cloud.google.com/tpu/docs/profile-tpu-vm

5. 2022. Supported reference models. https://cloud.google.com/tpu/docs/tutorials/supported-models 2022. Supported reference models. https://cloud.google.com/tpu/docs/tutorials/supported-models

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