How FCA Doubles BERT’s Inference Speed with Less Than 1% Accuracy Loss

This article explains how the Fine‑ and Coarse‑Granularity Hybrid Self‑Attention (FCA) mechanism reduces BERT’s computational cost by over 50% while keeping accuracy loss under 1%, detailing the method, experimental results, and its significance for efficient large‑scale language models.

BERTDeep LearningFCA

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How FCA Doubles BERT’s Inference Speed with Less Than 1% Accuracy Loss