Summary of Towards Next-level Post-training Quantization Of Hyper-scale Transformers, by Junhan Kim et al.
Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers
by Junhan Kim, Chungman Lee, Eulrang Cho, Kyungphil Park, Ho-young Kim, Joonyoung Kim, Yongkweon Jeon
First submitted to arxiv on: 14 Feb 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 PTQ algorithm, aespa, balances accuracy and efficiency by performing layer-wise quantization for efficiency while targeting attention-wise reconstruction to consider cross-layer dependencies. Aespa is designed for deploying hyper-scale Transformers on edge devices like mobile phones and TVs. The paper demonstrates the effectiveness of aespa through extensive experiments on various language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make big AI models work well on small devices, like your phone or TV. The problem was that existing methods took too long and used too many resources. Aespa is faster and uses less energy because it works layer by layer, but also takes into account how different parts of the model talk to each other. |
Keywords
* Artificial intelligence * Attention * Quantization
