@inproceedings{ea9c38be3b754aff8dc9b4f04540c9ba,
title = "Analysis of Hardware Prefetchers Suitable for CNN Applications",
abstract = "Employing CPUs for convolutional neural networks (CNNs) is of interest to many users due to its availability. However, to make CPUs more competitive, it is necessary to bridge the performance gap between CPUs and other accelerators such as GPUs and TPUs. Date prefetching is the promising optimization technique because CNN operations tend to have predictable data access patterns. This paper examines and analyzes the usefulness of various hardware prefetchers in CNN applications.",
keywords = "Convolutional Neural Network, Hardware Prefetcher, McSimA+",
author = "Seong, \{Hyeong Gi\} and Hyokeun Lee and Hyun Kim and Lee, \{Hyuk Jae\}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021 ; Conference date: 01-11-2021 Through 03-11-2021",
year = "2021",
doi = "10.1109/ICCE-Asia53811.2021.9641920",
language = "English",
series = "2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021",
}