Summary of Openls-dgf: An Adaptive Open-source Dataset Generation Framework For Machine Learning Tasks in Logic Synthesis, by Liwei Ni et al.
OpenLS-DGF: An Adaptive Open-Source Dataset Generation Framework for Machine Learning Tasks in Logic Synthesis
by Liwei Ni, Rui Wang, Miao Liu, Xingyu Meng, Xiaoze Lin, Junfeng Liu, Guojie Luo, Zhufei Chu, Weikang Qian, Xiaoyan Yang, Biwei Xie, Xingquan Li, Huawei Li
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 OpenLS-DGF, an adaptive logic synthesis dataset generation framework, enhances machine learning applications within the logic synthesis process. The framework encapsulates three fundamental steps of logic synthesis: Boolean representation, logic optimization, and technology mapping. It generates datasets in Verilog and GraphML formats, preserving original information. Researchers can customize the generated dataset by inserting additional steps and refining it incrementally. OpenLS-DGF also includes an adaptive circuit engine for managing the final dataset and downstream tasks. The generated OpenLS-D-v1 dataset comprises 46 combinational designs from established benchmarks, totaling over 966,000 Boolean circuits. The framework is versatile and supports integrating new data features, making it suitable for new challenges. This paper demonstrates the versatility of OpenLS-D-v1 through four distinct downstream tasks: circuit classification, circuit ranking, quality of results (QoR) prediction, and probability prediction. Each task represents essential steps of logic synthesis, and the experimental results show that the generated dataset achieves prominent diversity and applicability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary OpenLS-DGF is a new tool that helps improve how computer chips are designed. It makes it easier for researchers to create special datasets for designing computer chips. This tool has three main parts: making Boolean representations, optimizing logic, and mapping technology. The tool also saves the original information in two formats: Verilog and GraphML. Researchers can customize the generated dataset by adding more steps or refining it. OpenLS-DGF also helps with managing the final dataset and doing downstream tasks. The tool created a big dataset called OpenLS-D-v1 that has over 966,000 Boolean circuits from established benchmarks. This paper shows how OpenLS-D-v1 works for four different tasks: classifying circuits, ranking circuits, predicting results, and predicting probabilities. |
Keywords
» Artificial intelligence » Classification » Machine learning » Optimization » Probability
