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arXiv · 2409.10489

Flash STU: Fast Spectral Transform Units

Y. Isabel Liu·Windsor Nguyen·Yagiz Devre·Evan Dogariu·Anirudha Majumdar·Elad Hazan
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Abstract

Recent advances in state-space model architectures have shown great promise for efficient sequence modeling, but challenges remain in balancing computational efficiency with model expressiveness. We propose the Flash STU architecture, a hybrid model that interleaves spectral state space model layers with sliding window attention, enabling scalability to billions of parameters for language modeling while maintaining a near-linear time complexity. We evaluate the Flash STU and its variants on diverse sequence prediction tasks, including linear dynamical systems, robotics control, and language modeling. We find that, given a fixed parameter budget, the Flash STU architecture consistently outperforms the Transformer and other leading state-space models such as S4 and Mamba-2.

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Y. Isabel Liu, Windsor Nguyen, Yagiz Devre, Evan Dogariu, Anirudha Majumdar, Elad Hazan. 2024-09-16. Flash STU: Fast Spectral Transform Units. https://doi.org/10.1109/cdc57313.2025.11312729

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