Decision Making in Synthesis cross Technologies using LSTMs and Transfer Learning-Reference-Cited by-同舟云学术

Decision Making in Synthesis cross Technologies using LSTMs and Transfer Learning

Published:2020-11-16 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2020 ACM/IEEE Workshop on Machine Learning for CAD
language:
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Author:

Yu Cunxi¹,Zhou Wang²

Affiliation:

1. University of Utah, Salt Lake City, UT, USA

2. IBM Thomas J. Watson Research Center, New York, NY, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3380446.3430638

Reference18 articles.

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2. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation Vol. 9 8 (1997) 1735--1780. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation Vol. 9 8 (1997) 1735--1780.

3. Abdelrahman Hosny Soheil Hashemi Mohamed Shalan and Sherief Reda. 2019. DRiLLS: Deep Reinforcement Learning for Logic Synthesis. arXiv preprint arXiv:1911.04021 (2019). Abdelrahman Hosny Soheil Hashemi Mohamed Shalan and Sherief Reda. 2019. DRiLLS: Deep Reinforcement Learning for Logic Synthesis. arXiv preprint arXiv:1911.04021 (2019).

4. Nachiket Kapre Harnhua Ng Kirvy Teo and Jaco Naude. 2015. InTime: A Machine Learning Approach for Efficient Selection of FPGA CAD Tool Parameters . (Feb. 2015). Nachiket Kapre Harnhua Ng Kirvy Teo and Jaco Naude. 2015. InTime: A Machine Learning Approach for Efficient Selection of FPGA CAD Tool Parameters . (Feb. 2015).

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