Summary of Amsnet-kg: a Netlist Dataset For Llm-based Ams Circuit Auto-design Using Knowledge Graph Rag, by Yichen Shi et al.
AMSnet-KG: A Netlist Dataset for LLM-based AMS Circuit Auto-Design Using Knowledge Graph RAG
by Yichen Shi, Zhuofu Tao, Yuhao Gao, Tianjia Zhou, Cheng Chang, Yaxing Wang, Bingyu Chen, Genhao Zhang, Alvin Liu, Zhiping Yu, Ting-Jung Lin, Lei He
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Hardware Architecture (cs.AR); Emerging Technologies (cs.ET); Signal Processing (eess.SP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes an automated approach for designing high-performance analog and mixed-signal (AMS) circuits using large language models (LLMs). The current design process is experience-driven, making it challenging to automate. To address this issue, the authors introduce AMSnet-KG, a dataset containing various AMS circuit schematics and netlists with detailed functional and performance characteristics. This dataset enables the construction of a knowledge graph that facilitates the automated design flow. The approach involves formulating a design strategy based on required specifications, retrieving matched circuit components, and refining the topology through Bayesian optimization. Simulation results are fed back to the LLM for further refinement. Case studies demonstrate the effectiveness of this approach in designing operational amplifiers and comparators with minimal human effort. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special dataset called AMSnet-KG that helps design analog circuits using computers. Right now, humans do most of the work when designing these circuits, which takes a lot of time. The authors want to make it easier by using powerful computer tools called large language models (LLMs). They give the LLMs information about different circuit designs and how they work. Then, the LLMs help design new circuits that meet certain requirements. This approach can be used for designing important parts like amplifiers or comparators. |
Keywords
» Artificial intelligence » Knowledge graph » Optimization
