Summary of Turboattention: Efficient Attention Approximation For High Throughputs Llms, by Hao Kang et al.
TurboAttention: Efficient Attention Approximation For High Throughputs LLMs
by Hao Kang, Srikant Bharadwaj, James Hensman, Tushar Krishna, Victor Ruhle, Saravan Rajmohan
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
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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 This paper addresses the computational and memory demands of large language model (LLM) inference, particularly in the attention mechanism. Techniques like FlashAttention have improved efficiency by accelerating execution, but require high-precision formats. To overcome this limitation, recent Key-value (KV) cache quantization reduces memory bandwidth, yet still necessitates floating-point dequantization for attention operation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are powerful tools that can process and understand human-like text. However, they require a lot of computer power and memory to work efficiently. Scientists have developed ways to make them faster, like the “FlashAttention” method, but this still uses a lot of memory. The researchers in this paper looked at another way to speed up language models: by reducing the amount of data that needs to be stored in memory. |
Keywords
» Artificial intelligence » Attention » Inference » Large language model » Precision » Quantization
