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

• المؤلفون الرئيسيون: Huai, Shuo, Kong, Hao, Luo, Xiangzhong, Li, Shiqing, Subramaniam, Ravi, Makaya, Christian, Lin, Qian, Liu, Weichen
• مؤلفون آخرون: School of Computer Science and Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2023
• الموضوعات: Engineering::Computer science and engineering; In-Memory Processing; Pruning
• الوصول للمادة أونلاين: 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...