Qian Lin
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This paper proposes a dynamic image cropping framework and compound shrinking strategy to reduce computational overhead of CNNs on edge AI, achieving higher accuracy and lower computational cost.
This paper addresses the obstacles of using Crossbar-based In-Memory Processing (IMP) accelerators for deep neural networks (DNNs) by reusing bit-shift units for multiplication, applying pruning methods, and adapting to non-ideality.
Papers
CRIMP: Compact & Reliable DNN Inference on In-Memory Processing via Crossbar-Aligned Compression and Non-ideality Adaptation
Shuo Huai, Hao Kong, Xiangzhong Luo, Shiqing Li +4 more
This paper addresses the obstacles of using Crossbar-based In-Memory Processing (IMP) accelerators for deep neural networks (DNNs) by reusing bit-shift units for multiplication, applying pruning metho…