Automated accelerator optimization aided by graph neural networks-Reference-Cited by-同舟云学术

Automated accelerator optimization aided by graph neural networks

Published:2022-07-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 59th ACM/IEEE Design Automation Conference
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Author:

Sohrabizadeh Atefeh¹,Bai Yunsheng¹,Sun Yizhou¹,Cong Jason¹

Affiliation:

1. University of California

Funder

NSF (National Science Foundation)

Intel Corporation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3489517.3530409

Reference26 articles.

1. J. Cong etal 2016. Source-to-source optimization for HLS. In FPGAs for Software Programmers. 137--163. J. Cong et al. 2016. Source-to-source optimization for HLS. In FPGAs for Software Programmers. 137--163.

2. J. Cong etal 2016. Software infrastructure for enabling FPGA-based accelerations in data centers. In ISLPED. 154--155. J. Cong et al. 2016. Software infrastructure for enabling FPGA-based accelerations in data centers. In ISLPED. 154--155.

3. C. Cummins etal 2021. ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations. (2021). C. Cummins et al. 2021. ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations. (2021).

4. Fast inference of deep neural networks in FPGAs for particle physics

5. D. P. Kingma 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). D. P. Kingma et al. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).

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