Researchers have developed a novel co-evolutionary algorithm that uses a large language model (LLM) to design approximate multipliers for circuit approximation. This method automates the optimization process without needing domain-specific LLM training. The algorithm simultaneously evolves candidate circuits and prompt templates to guide the LLM's modifications, achieving better error-area trade-offs than existing optimized libraries for various design objectives. AI
RANK_REASON Academic paper detailing a new method for circuit approximation using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
Read on arXiv cs.NE (Neural & Evolutionary) →
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