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LLM-driven co-evolutionary algorithm designs optimized approximate multipliers

By PulseAugur Editorial · [1 sources] ·

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) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM-driven co-evolutionary algorithm designs optimized approximate multipliers

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Academic paper detailing a new method for circuit approximation using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Lukas Sekanina ·

    Multi-Objective Coevolution of Prompts and Templates for Circuit Approximation

    Approximate multipliers deliberately relax computational accuracy to achieve gains in power efficiency, latency, and silicon area, which makes them well-suited for error-resilient applications such as neural networks. In this work, we introduce a co-evolutionary algorithm that le…

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