Author

Hanyong Shao

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#machine learning Preprint Oct 2026

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 de...

Wen-Shuai Yao, Wen-Yong Zhou, Han-Yong Shao et al. · 0 citations

Jul 2026

Recall Before You Rank: Similarity-Guided Top-K Reuse for Efficient Long-Context Attention

ReTopK is a training-free method that accelerates dynamic Top-$K$ attention by reusing historical retrieval decisions and retains the complete KV cache and reuses only selected indices, rather than historical scores, attention weights, or outputs.

Wen-Shuai Yao, Wen-Yong Zhou, Hanyong Shao et al. · arXiv.org · 0 citations

Jul 2026

CoSA: Accelerating Long-Context Inference via Proxy-Kernel Co-Designed Sparse Attention

CoSA is proposed, a two-stage training-free Sparse Attention under proxy-kernel CO-design, which couples a Kernel-Aware Proxy (KAP) with an Ordered-Skipping Kernel (OSK) and achieves a 4.93% attention speedup and reduces end-to-end Time-to-First-Token by 2.53% with negligible performance degradation.

Yufei Xue, Lin Niu, Hong Liu et al. · arXiv.org · 0 citations