"η-LSTM: Co-Designing Highly-Efficient Large LSTM Training via ..."

Xingyao Zhang et al. (2021)

> Home

Details and statistics

DOI: 10.1109/ISCA52012.2021.00051

access: closed

type: Conference or Workshop Paper

metadata version: 2026-04-09

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - conf/isca/ZhangXZSFTS21
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/conf/isca/ZhangXZSFTS21
Xingyao Zhang, Haojun Xia, Donglin Zhuang, Hao Sun, Xin Fu, Michael B. Taylor, Shuaiwen Leon Song:
η-LSTM: Co-Designing Highly-Efficient Large LSTM Training via Exploiting Memory-Saving and Architectural Design Opportunities. ISCA 2021: 567-580

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.