New framework automates transformer approximation for faster homomorphic encryption

Researchers have developed ATLAS, an automated framework designed to optimize the approximation of transformer models for efficient homomorphic inference. This new system addresses the challenge of configuring per-layer approximation settings, which is crucial for reducing latency and maintaining predictive accuracy in fully homomorphic encryption (FHE). ATLAS employs a two-stage optimization strategy and surrogate models to navigate the vast search space of possible configurations, making it feasible to deploy complex models like BERT, ViT, and LLaMA 3 under FHE. AI

IMPACT Enables more efficient and private inference of large transformer models using homomorphic encryption.

RANK_REASON This is a research paper detailing a new method for optimizing AI models for a specific computational task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Bert
• CKKS
• homomorphic encryption
• Llama 3
• transformers
• Vít

• paper
• infra

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