Summary of Blast: Block-level Adaptive Structured Matrices For Efficient Deep Neural Network Inference, by Changwoo Lee et al.
BLAST: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference
by Changwoo Lee, Soo Min Kwon, Qing Qu, Hun-Seok Kim
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 introduces the Block-Level Adaptive STructured (BLAST) matrix, designed to learn and leverage efficient structures prevalent in the weight matrices of linear layers within deep learning models. This innovation aims to address computational challenges during inference in large-scale foundation models. By using BLAST weights, researchers can compress medium-sized models like ViT and GPT-2 by 70% and 40%, respectively, while maintaining performance. For larger models like Llama-7B and DiT-XL, the BLAST matrix achieves a 2x compression with minimal performance degradation. The code is available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make big computer programs faster and more efficient. These programs are called deep learning models. They’re like super-powerful computers that can do many tasks, like recognizing pictures or understanding language. But they use a lot of energy and computing power. The new method, called BLAST, helps make these programs smaller and faster while still doing a good job. It works by finding patterns in the way the program does calculations and using those patterns to make it work more efficiently. |
Keywords
» Artificial intelligence » Deep learning » Gpt » Inference » Llama » Vit
