Summary of Verified Lifting Of Deep Learning Operators, by Qi Zhan et al.
Verified Lifting of Deep learning Operators
by Qi Zhan, Xing Hu, Xin Xia, Shanping Li
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Programming Languages (cs.PL); 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 A novel framework for verifying the functionality of deep learning operators is introduced, bridging the gap between low-level implementations and high-level mathematical formulas. The framework combines symbolic execution, syntax-guided synthesis, and SMT-based verification to produce readable and formally verified summaries. Employing top-down and bottom-up strategies in synthesis and leveraging SMT solvers in verification, this approach demonstrates effectiveness compared to existing techniques on a dataset of deep learning operators implemented in Triton. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps developers create their own customized deep learning operators by providing a tool that can summarize the functionality of both existing and user-defined operators. The framework combines symbolic execution, syntax-guided synthesis, and SMT-based verification to produce readable and formally verified mathematical formulas. This makes it easier for developers to understand and use these operators. |
Keywords
» Artificial intelligence » Deep learning » Syntax
