Part 3/10:
The Mechanism: Maximal Parallelization
In essence, Hydra Attention uses as many attention heads as features—a stark contrast to conventional methods that typically use 1, 8, 16, or 32 heads. This approach leverages massive parallelism, enabling the entire attention computation to run more efficiently and significantly faster than standard self-attention. The results? Speed improvements by a factor of up to 197 times, according to the study.
Key Results and Performance
The paper highlights promising outcomes: