MOAI framework boosts secure transformer inference with optimized HE

Researchers have developed MOAI, a novel framework designed to enhance the efficiency of non-interactive secure transformer inference using homomorphic encryption (HE). MOAI addresses the computational overhead of HE by optimizing the evaluation flow, employing rotation-free algorithms for Softmax and LayerNorm, and reducing HE rotations in matrix multiplications. This approach leads to a significant reduction in evaluation time compared to existing methods, with potential applications demonstrated on models like BERT-base, Powerformer, and LLaMA-3-8B. AI

IMPACT Enhances privacy in LLM inference by significantly reducing computational overhead for homomorphic encryption.

RANK_REASON The item is a research paper detailing a new technical framework for secure transformer inference. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Association for Computational Linguistics
• homomorphic encryption
• LayerNorm
• LLaMA-3-8B
• MOAI
• Moon et al.
• Park et al.
• Powerformer
• Softmax
• THOR
• transformer inference

• paper
• infra

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