A Case Study for Improving Performances of Deep-Learning Processor with MRAM-Reference-Cited by-同舟云学术

A Case Study for Improving Performances of Deep-Learning Processor with MRAM

Published:2024 Issue:0 Volume:17 Page:7-15
ISSN:1882-6687
Container-title:IPSJ Transactions on System and LSI Design Methodology
language:en
Short-container-title:IPSJ Transactions on System LSI Design Methodology

Author:

Ohara Ryotaro¹,Fukunaga Atsushi²,Taichi Masakazu¹,Kabuto Masaya²,Hamabe Riku¹,Ikegawa Masato¹,Izumi Shintaro²,Kawaguchi Hiroshi²

Affiliation:

1. Graduate School of Science, Technology and Innovation, Kobe University

2. Graduate School of System Informatics, Kobe University

Publisher

Information Processing Society of Japan

Link

https://www.jstage.jst.go.jp/article/ipsjtsldm/17/0/17_7/_pdf

Reference23 articles.

1. [1] Xu, X., Ding, Y., Hu, S.X., Niemier, M., Cong, J., Hu, Y. and Shi, Y.: Scaling for Edge Inference of Deep Neural Networks, Nat Electron, Vol.1, No.4, pp.216-222, April 2018, DOI: 10.1038/s41928-018-0059-3.

2. [2] Fu, C.-Y., Liu, W., Ranga, A., Tyagi, A. and Berg, A.C.: DSSD: Deconvolutional Single Shot Detector, arXiv preprint arXiv:1701.06659, Jan. 2017.

3. [3] Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. and Chen, L.-C.: MobileNetV2: Inverted Residuals and Linear Bottlenecks, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.4510-4520, June 2018, DOI: 10.1109/CVPR.2018.00474.

4. [4] Wang, C.-Y., Bochkovskiy, A. and M. Liao, H.-Y.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv: 2207.02696, 2022.

5. [5] Han, S., Liu, X., Mao, H., Pu, J., Pedram, A., Horowitz, M.A. and Dally, W.J.: EIE: efficient inference engine on compressed deep neural network, ACM SIGARCH Computer Architecture News, Vol.44, No.3, pp.243-254, Oct. 2016, DOI: 10.1145/3007787.3001163.