Quantifying the Influences of Data Prefetching Using Artificial Neural Networks
- https://doi.org/10.2991/acaai-18.2018.40How to use a DOI?
- cache misses; data prefetching; artificial neural networks
Data prefetching has been widely used in modern cache subsystems. Actually, an aggressive prefetching may bring negative yields unexpectedly, in which a new proposed prefetching strategy normally needs to be evaluated before being applied in the real design. In the last decade, prior researchers prefer to utilize the cycle-accurate simulations or trace-driven simulations to study the prefetching behaviors. However, as the increasing complexity of hardware components, the huge time-consuming simulation-based methods would never be appropriate for performance evaluations. This paper proposes a method of modeling prefetching influences on cache misses using artificial neural networks, which has an average error of 8% compared to gem5 cycle-accurate simulations, and the performance prediction process can be sped up by 30 times on average.
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Kecheng Ji AU - Li Liu PY - 2018/03 DA - 2018/03 TI - Quantifying the Influences of Data Prefetching Using Artificial Neural Networks BT - Proceedings of the 2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI 2018) PB - Atlantis Press SP - 170 EP - 172 SN - 1951-6851 UR - https://doi.org/10.2991/acaai-18.2018.40 DO - https://doi.org/10.2991/acaai-18.2018.40 ID - Ji2018/03 ER -