LampAttention: Look-Ahead Mixed-Precision FlashAttention for Dedicated Accelerators

Abstract:While most attention logits can be computed in low precision without degrading numerical stability, current attention kernels fail to exploit this phenomenon. We introduce a novel hardware-algorithm co-design in the form of mixed-precision FlashAttention. Our method accumulates key-query products and evaluates their exponentials in 8-bit formats, then adaptively identifies sensitive sub-blocks and recomputes them in 16-bit formats. We propose the specifications for a dedicated accelerator capable of executing this pipeline efficiently. Simulated experiments with Qwen3 and Gemma 3 show that rerouting a selective minority of sub-blocks to high precision is sufficient to recover the baseline model performance.

Machine Learning (cs.LG); Numerical Analysis (math.NA)

arXiv:2609.39361 [cs.LG]

(or arXiv:2609.39361v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.39361 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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