Summary of Boa: Attention-aware Post-training Quantization Without Backpropagation, by Junhan Kim et al.
BoA: Attention-aware Post-training Quantization without Backpropagation
by Junhan Kim, Ho-young Kim, Eulrang Cho, Chungman Lee, Joonyoung Kim, Yongkweon Jeon
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed novel backpropagation-free PTQ algorithm optimizes integer weights by considering inter-layer dependencies in large language models. The approach uses attention-aware Hessian matrices to capture interactions within the attention module, outperforming existing methods and showing synergy with conventional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can be deployed on resource-constrained devices using post-training quantization (PTQ). A new algorithm optimizes integer weights by considering inter-layer dependencies in attention modules. This approach outperforms existing methods and works well with others to reduce activation outliers. |
Keywords
» Artificial intelligence » Attention » Backpropagation » Quantization
