CRIMP: compact & reliable DNN inference on in-memory processing via crossbar-aligned compression and non-ideality adaptation

• Main Authors: Huai, Shuo, Kong, Hao, Luo, Xiangzhong, Li, Shiqing, Subramaniam, Ravi, Makaya, Christian, Lin, Qian, Liu, Weichen
• 其他作者: School of Computer Science and Engineering
• 格式: Article
• 語言: English
• 出版: 2023
• 主題: Engineering::Computer science and engineering; In-Memory Processing; Pruning
• 在線閱讀: https://hdl.handle.net/10356/171633

Crossbar-based In-Memory Processing (IMP) accelerators have been widely adopted to achieve high-speed and low-power computing, especially for deep neural network (DNN) models with numerous weights and high computational complexity. However, the floating-point (FP) arithmetic is not compatible with c...