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arXiv · 2606.13089

Multi-Objective Coevolution of Prompts and Templates for Circuit Approximation

Martin Tomasovic·Lukas Sekanina
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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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Martin Tomasovic, Lukas Sekanina. 2026-06-11. Multi-Objective Coevolution of Prompts and Templates for Circuit Approximation. https://arxiv.org/abs/2606.13089

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