ABNAT: Attention-Based Noise-Aware Training for Robust Transformers on Analog Compute-in-Memory Systems
Transformer-based models have become the backbone of a wide range of tasks, but their intensive computation and data movement incur substantial energy overhead. Analog compute-in-memory (CIM) offers an energy-efficient alternative, yet its inherent hardware non-idealities perturb stored weights and can significantly degrade model performance. Although existing noise-aware training (NAT) methods improve robustness by injecting noise during training, they largely overlook the critical role of attention mechanisms in Transformer inference.To bridge this gap, we analyze how CIM-induced noise reshapes attention in Transformers and identify two key observations: (1) the divergence between clean...
| Authors | Wenshuai Yao, Wenyong Zhou, Hanyong Shao et al. |
|---|---|
| Primary category | cs.LG |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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