Multi-Objective Coevolution of Prompts and Templates for Circuit Approximation

• URL: http://arxiv.org/abs/2606.13089v1
• Date: Thu, 11 Jun 2026 09:14:37 GMT
• Title: Multi-Objective Coevolution of Prompts and Templates for Circuit Approximation
• Abstract summary: We introduce a co-evolutionary algorithm that leverages an off-the-shelf large language model (LLM) without requiring domain-specific training to automate the design of optimized 8-bit approximate multipliers.<n> Experimental results for several target design objectives demonstrate that the proposed method discovers approximate multipliers with improved error-area trade-offs.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 leverages an off-the-shelf large language model (LLM) without requiring domain-specific training to automate the design of optimized 8-bit approximate multipliers. The approach simultaneously evolves a population of candidate circuits and a population of prompt templates that steer LLM-driven modifications. Experimental results for several target design objectives demonstrate that the proposed method discovers approximate multipliers with improved error-area trade-offs compared to highly optimized circuits from the EvoApproxLib library.

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