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

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

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...

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Bibliographic Details
Main Authors: Huai, Shuo, Kong, Hao, Luo, Xiangzhong, Li, Shiqing, Subramaniam, Ravi, Makaya, Christian, Lin, Qian, Liu, Weichen
Other Authors: School of Computer Science and Engineering
Format: Journal Article
Language:English
Published: 2023
Subjects:
Engineering::Computer science and engineering
In-Memory Processing
Pruning
Online Access:https://hdl.handle.net/10356/171633

Internet

https://hdl.handle.net/10356/171633

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