Summary of Exaq: Exponent Aware Quantization For Llms Acceleration, by Moran Shkolnik et al.
EXAQ: Exponent Aware Quantization For LLMs Acceleration
by Moran Shkolnik, Maxim Fishman, Brian Chmiel, Hilla Ben-Yaacov, Ron Banner, Kfir Yehuda Levy
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Performance (cs.PF)
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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 explores novel methods for optimizing the inference process of Large Language Models (LLMs) to reduce computational and storage expenses. By leveraging quantization techniques, researchers have focused on reducing weights and activations to enable low-bit general-matrix-multiply (GEMM) operations. However, this approach still leaves room for improvement, particularly in the softmax layer, which remains a significant bottleneck. To address this issue, the authors propose an analytical approach to determine the optimal clipping value for the input to the softmax function, enabling sub-4-bit quantization for LLMs inference. This technique accelerates calculations of both exponential and accumulation phases with minimal accuracy degradation. The paper demonstrates its effectiveness by achieving baseline performance in 2-bit quantization on the PIQA dataset evaluation, resulting in a 36.9% acceleration in the softmax operation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super-smart computers that can understand human language and generate responses. Right now, these models take up lots of computer power and storage space. Scientists have been trying to make them more efficient by “quantizing” them – like shrinking a big house into a small one. But there’s still a problem: the part of the model that does math to figure out answers is slow. This paper shows how to fix this issue by finding the right way to do math in a tiny amount of computer power and storage space. It uses a special formula to make the math faster without losing accuracy. The result is super-fast LLMs that can understand our language better than ever before! |
Keywords
» Artificial intelligence » Inference » Quantization » Softmax
