Effective and Efficient Microprocessor Design Space Exploration Using Unlabeled Design Configurations
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1671-1676.
Abstract
During the design of a microprocessor, Design Space Exploration (DSE) is a critical step which determines the appropriate design configuration of the microprocessor. In the computer architecture community, supervised learning techniques have been applied to DSE to build models for predicting the qualities of design configurations. For supervised learning, however, considerable simulation costs are required for attaining the labeled design configurations. Given limited resources, it is difficult to achieve high accuracy. In this paper, inspired by recent advances in semi-supervised learning, we propose the COMT approach which can exploit unlabeled design configurations to improve the models. In addition to an improved predictive accuracy, COMT is able to guide the design of microprocessors, owing to the use of comprehensible model trees. Empirical study demonstrates that COMT significantly outperforms state-of-the-art DSE technique through reducing mean squared error by 30% to 84%, and thus, promising architectures can be attained more efficiently.
@inproceedings{ijcai2011p281,
title = {Effective and Efficient Microprocessor Design Space Exploration Using Unlabeled Design Configurations},
author = {Guo, Qi and Chen, Tianshi and Chen, Yunji and Zhou, Zhi-Hua and Hu, Weiwu and Xu, Zhiwei},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {1671--1676},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-281},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-281},
}
TY - CPAPER TI - Effective and Efficient Microprocessor Design Space Exploration Using Unlabeled Design Configurations AU - Guo, Qi AU - Chen, Tianshi AU - Chen, Yunji AU - Zhou, Zhi-Hua AU - Hu, Weiwu AU - Xu, Zhiwei T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 1671 EP - 1676 DO - 10.5591/978-1-57735-516-8/IJCAI11-281 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-281 ER -Copied
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