ELSA: Exact Linear-Scan Attention for Fast and Memory-Light Vision Transformers
Existing attention accelerators often trade exact softmax semantics, depend on fused Tensor Core kernels, or incur sequential depth that limits FP32 throughput on long sequences. We present \textbf{ELSA}, an algorithmic reformulation of online softmax attention that (i)~preserves exact softmax semantics in real arithmetic with a \emph{provable} $\mathcal{O}(u\log n)$ FP32 relative error bound; (ii)~casts the online softmax update as a prefix scan over an associative monoid $(m,S,W)$, yielding $O(n)$ extra memory and $O(\log n)$ parallel depth; and (iii)~is Tensor-Core independent, implemented in Triton and CUDA C++, and deployable as a \emph{drop-in replacement} requiring no retraining or...
| Authors | Chih-Chung Hsu, Xin-Di Ma, Wo-Ting Liao et al. |
|---|---|
| Primary category | cs.LG |
| Published | 26 April 2026 |
| Last updated at source | 26 April 2026 |
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