Summary of Ledro: Llm-enhanced Design Space Reduction and Optimization For Analog Circuits, by Dimple Vijay Kochar et al.
LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog Circuits
by Dimple Vijay Kochar, Hanrui Wang, Anantha Chandrakasan, Xin Zhang
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 LEDRO framework leverages Large Language Models in conjunction with optimization techniques to iteratively refine the design space for analog circuit sizing. Building upon Bayesian Optimization (BO) and Reinforcement Learning (RL), LEDRO exhibits superior performance, efficiency, and generalizability compared to existing baselines. The framework is evaluated on 22 different Op-Amp topologies across four FinFET technology nodes, demonstrating an average of 13% FoM improvement with a 2.15x speed-up on low complexity Op-Amps and 48% FoM improvement with a 1.7x speed-up on high complexity Op-Amps. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LEDRO is a new way to design analog circuits using large language models. This approach can help make the circuit design process faster and easier, without requiring experts in the field. The framework was tested on different types of amplifiers and technology nodes, showing significant improvements over existing methods. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
