Summary of Aero: Softmax-only Llms For Efficient Private Inference, by Nandan Kumar Jha and Brandon Reagen
AERO: Softmax-Only LLMs for Efficient Private Inference
by Nandan Kumar Jha, Brandon Reagen
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive analysis on the role of nonlinearities in transformer-based decoder-only language models for private inference (PI). The authors introduce AERO, an architectural optimization framework that refines the existing LLM architecture to remove nonlinearities and reduce FLOPs counts. They also propose a Softmax-only architecture tailored for efficient PI and devise an entropy regularization technique to improve its performance. The results show up to 4.23 times communication and 1.94 times latency reduction, outperforming state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem with language models that use sensitive data. It shows how to make these models work privately, without leaking information about the users’ personal data. The researchers developed a special framework called AERO that makes this possible by removing some extra steps in the model’s calculations. They also created a new type of model that uses only one kind of calculation, which is more efficient and private. |
Keywords
» Artificial intelligence » Decoder » Inference » Optimization » Regularization » Softmax » Transformer
