Summary of Chai: Clustered Head Attention For Efficient Llm Inference, by Saurabh Agarwal et al.
CHAI: Clustered Head Attention for Efficient LLM Inference
by Saurabh Agarwal, Bilge Acun, Basil Hosmer, Mostafa Elhoushi, Yejin Lee, Shivaram Venkataraman, Dimitris Papailiopoulos, Carole-Jean Wu
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel attention mechanism for Large Language Models (LLMs), which can reduce the memory requirements by up to 21.4% and inference time latency by up to 1.73x without sacrificing accuracy. The proposed Clustered Head Attention (CHAI) combines heads with high correlation, reducing both memory and compute needs. The authors demonstrate CHAI’s effectiveness on three LLMs and five evaluation datasets, achieving a maximum deviation of 3.2% in accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models have revolutionized the field of machine learning, but they require significant computational resources to run at inference time. This paper solves this problem by introducing Clustered Head Attention (CHAI), which reduces memory and compute needs without sacrificing performance. CHAI combines correlated attention heads, making it an efficient way to process large amounts of data. |
Keywords
* Artificial intelligence * Attention * Inference * Machine learning
