Summary of Slanc: Static Layernorm Calibration, by Mahsa Salmani et al.
SLaNC: Static LayerNorm Calibration
by Mahsa Salmani, Nikita Trukhanov, Ilya Soloveychik
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 proposes a computationally-efficient scaling technique to address the issue of calculating LayerNorm in Transformer models on hardware accelerators. The increasing size of Large Language Models (LLMs) has put pressure on manufacturers, leading to innovations in dedicated hardware design. To reduce compute, communication, and storage requirements, quantization techniques have become a focus area. However, this poses challenges due to limited value representations. The proposed method scales LayerNorm inputs based on static weights from preceding linear layers, computed offline without adding latency or overhead during inference. This approach ensures smooth, accurate, and resource-effective inference across various hardware architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding a way to make large language models work better on special computers that help process big data quickly. These computers are getting bigger too, so people are trying to find ways to make them more efficient. One problem they’re facing is how to calculate something called LayerNorm in these models. The researchers came up with an easy and fast way to do this using the weights from other parts of the model. This new technique can be used on different types of computers without slowing it down or causing problems. |
Keywords
» Artificial intelligence » Inference » Quantization » Transformer
