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cs.LG2025
Fast attention mechanisms: a tale of parallelism
Jingwen Liu, Hantao Yu, Clayton Sanford +2
Transformers have the representational capacity to simulate Massively Parallel Computation (MPC) algorithms, but they suffer from quadratic time complexity, which severely limits t…
cs.LG2025
Two Heads Are Better than One: Simulating Large Transformers with Small Ones
Hantao Yu, Josh Alman
The quadratic complexity of self-attention prevents transformers from scaling effectively to long input sequences. On the other hand, modern GPUs and other specialized hardware acc…
cs.LG2024
Fundamental Limitations on Subquadratic Alternatives to Transformers
Josh Alman, Hantao Yu
The Transformer architecture is widely deployed in many popular and impactful Large Language Models. At its core is the attention mechanism for calculating correlations between pai…