"A 12-nm 0.62-1.61 mW Ultra-Low Power Digital CIM-based Deep-Learning ..."

En-Jui Chang et al. (2023)

> Home

Details and statistics

DOI: 10.23919/VLSITECHNOLOGYANDCIR57934.2023.10185296

access: closed

type: Conference or Workshop Paper

metadata version: 2023-07-28

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - conf/vlsit/ChangXDJLCLLKJG23
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/conf/vlsit/ChangXDJLCLLKJG23
En-Jui Chang, Cheng-Xin Xue, Chetan Deshpande, Gajanan Jedhe, Jenwei Liang, Chih-Chung Cheng, Hung-Wei Lin, Chia-Da Lee, Sushil Kumar, Kim Soon Jway, Zijie Guo, Ritesh Garg, Allen-Cl Lu, Chien-Hung Lin, Meng-Han Hsieh, Tsung-Yao Lin, Chih-Cheng Chen:
A 12-nm 0.62-1.61 mW Ultra-Low Power Digital CIM-based Deep-Learning System for End-to-End Always-on Vision. VLSI Technology and Circuits 2023: 1-2

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.