Summary of Opal: Outlier-preserved Microscaling Quantization Accelerator For Generative Large Language Models, by Jahyun Koo et al.
OPAL: Outlier-Preserved Microscaling Quantization Accelerator for Generative Large Language Models
by Jahyun Koo, Dahoon Park, Sangwoo Jung, Jaeha Kung
First submitted to arxiv on: 6 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Hardware Architecture (cs.AR); 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 This paper presents an innovative hardware-software co-design method for large language models (LLMs) that focuses on reducing memory size and bandwidth usage while maintaining energy efficiency. The authors propose a novel activation quantization technique that leverages microscaling data format, preserves outliers per sub-tensor block, and utilizes mixed precision to optimize computations. This approach is implemented in an LLM accelerator called OPAL, which consists of FP units for handling outliers and vectorized INT multipliers for dominant operations. Additionally, OPAL employs log2-based approximation on softmax operations to maximize power efficiency. Experimental results show that the proposed method improves energy efficiency by 1.6-2.2x and reduces area by 2.4-3.1x with negligible accuracy loss. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to make big language models use less memory and energy while still working well. The researchers came up with a new way to reduce the size of activation values in these models, which helps save energy and space. They also used a combination of different precision levels for calculations to speed things up further. This approach was tested on an LLM accelerator called OPAL, which combines floating-point units for handling special cases and integer multipliers for most operations. The results show that this method can make the models use 60-70% less energy while keeping them just as accurate. |
Keywords
» Artificial intelligence » Precision » Quantization » Softmax
