用Transformer改进遗传算法，自动设计误差可控的低功耗电路
Genetic Programming with Transformer-Based Mutation for Approximate Circuit Design
- 用Transformer生成突变，替代传统随机突变
- 在多种误差约束下，性能优于现有最优电路设计
- 适合电路优化与创新设计者，可生成潜在专利方案
近年来，机器学习被用于提升进化设计与优化过程。本文提出一种基于Transformer的突变算子，用于笛卡尔遗传编程（CGP），实现近似算术电路的自动化设计。我们设计了一种混合机制，在标准突变与新突变之间切换，防止电路近似过程陷入停滞。同时，开发了新的训练方案，使用数千个代表不同近似乘法器的CGP染色体作为训练向量。在多个目标误差约束下，采用Transformer突变的CGP所演化出的近似乘法器，其性能优于当前最先进的EvoApproxLib库中的高度优化设计。尽管训练与进化过程计算开销较大，但似乎是提升现有近似电路、生成新型潜在可专利电路的必要步骤。
原文摘要 · Abstract (English)
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.
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