Genetic Programming with Transformer-Based Mutation for Approximate Circuit Design

2026-05-20

AI Summary

This work addresses the challenge of optimization stagnation in existing automated design methods for approximate arithmetic circuits, which often struggle to balance accuracy and hardware efficiency. To overcome this limitation, the study introduces a novel mutation operator that integrates the Transformer architecture into Cartesian Genetic Programming (CGP) for the first time. The proposed operator is trained on large-scale chromosome samples and embedded within a dynamic hybrid strategy that adaptively switches between standard and Transformer-based mutation during evolution. Evaluated on approximate multiplier synthesis, the method significantly enhances evolutionary efficiency and consistently yields designs that outperform the best entries in EvoApproxLib across multiple error constraints, achieving superior trade-offs among accuracy, area, and power consumption. These results demonstrate both notable innovation and strong practical potential.

Abstract

A recent trend is to leverage machine learning models to improve the evolutionary design and optimization process. We propose a novel transformer-based mutation operator for Cartesian genetic programming (CGP) for the automated design of approximate arithmetic circuits. We introduce a hybrid scheme for CGP in which the proposed mutation operator is switched with the standard mutation operator to prevent stagnation of the circuit approximation process. We also develop a new training scheme for the underlying transformer that utilizes training vectors composed of thousands of CGP chromosomes representing various approximate multipliers. For several target error constraints, the approximate multipliers evolved with CGP utilizing the transformer-based mutation achieve better trade-offs than the highly optimized designs available in the state-of-the-art EvoApproxLib library of approximate circuits. Although both training and evolutionary processes are computationally demanding, they appear to be necessary steps for improving existing approximate circuits and producing new, potentially patentable circuit designs.

Problem

• approximate circuit design
• evolutionary optimization
• genetic programming
• circuit approximation
• multiplier design

Innovation

• Transformer-based mutation
• Cartesian Genetic Programming
• Approximate Circuit Design
• Evolutionary Optimization
• Neural-guided Search