POPA: Expressing High and Portable Performance across Spatial and Vector Architectures for Tensor Computations-Reference-Cited by-同舟云学术

POPA: Expressing High and Portable Performance across Spatial and Vector Architectures for Tensor Computations

Published:2024-04 Issue: Volume:10 Page:199-210
ISSN:
Container-title:Proceedings of the 2024 ACM/SIGDA International Symposium on Field Programmable Gate Arrays
language:
Short-container-title:

Author:

Hao Xiaochen¹^ORCID,Rong Hongbo²^ORCID,Zhang Mingzhe³^ORCID,Sun Ce⁴^ORCID,Jiang Hong⁵^ORCID,Liang Yun⁶^ORCID

Affiliation:

1. Peking University, Beijing, China

2. Parallel Computing Lab, Intel, Santa Clara, USA

3. Tsinghua University, Beijing, China

4. University of Science and Technology of China, Hefei, China

5. Intel, Santa Clara, USA

6. Peking University & Beijing Advanced Innovation Center for Integrated Circuits, Beijing, China

Funder

the National Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626202.3637566

Reference63 articles.

1. Mart'in Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. Tensorflow: A system for large-scale machine learning. In Symposium on Operating Systems Design and Implementation (OSDI'16).

2. Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M Kakade, and Matus Telgarsky. 2014. Tensor decompositions for learning latent variable models. The Journal of Machine Learning Research (2014).

3. Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code

4. Machine Learning Systems are Stuck in a Rut

5. Stateful dataflow multigraphs