With the continuous advancement of processors, modern micro-architecture designs have become increasingly complex. The vast design space presents significant challenges for human designers, making design space exploration (DSE) algorithms a significant tool for $\mu$-arch design. In recent years, efforts have been made in the development of DSE algorithms, and promising results have been achieved. However, the existing DSE algorithms, e.g., Bayesian Optimization and ensemble learning, suffer from poor interpretability, hindering designers' understanding of the decision-making process. To address this limitation, we propose utilizing Fuzzy Neural Networks to induce and summarize knowledge and insights from the DSE process, enhancing interpretability and controllability. Furthermore, to improve efficiency, we introduce a multi-fidelity reinforcement learning approach, which primarily conducts exploration using cheap but less precise data, thereby substantially diminishing the reliance on costly data. Experimental results show that our method achieves excellent results with a very limited sample budget and successfully surpasses the current state-of-the-art. Our DSE framework is open-sourced and available at https://github.com/fanhanwei/FNN\_MFRL\_ArchDSE/\ .

翻译：随着处理器的持续进步，现代微架构设计变得日益复杂。庞大的设计空间对人类设计者构成了重大挑战，使得设计空间探索（DSE）算法成为微架构设计的重要工具。近年来，DSE算法的开发取得了进展，并获得了有前景的结果。然而，现有的DSE算法，例如贝叶斯优化和集成学习，存在可解释性差的问题，阻碍了设计者对决策过程的理解。为解决这一局限，我们提出利用模糊神经网络来归纳和总结DSE过程中的知识与洞见，从而增强可解释性和可控性。此外，为提高效率，我们引入了一种多保真度强化学习方法，该方法主要利用廉价但精度较低的数据进行探索，从而大幅减少对昂贵数据的依赖。实验结果表明，我们的方法在极其有限的样本预算下取得了优异的结果，并成功超越了当前最先进的技术。我们的DSE框架已开源，可在 https://github.com/fanhanwei/FNN\_MFRL\_ArchDSE/ 获取。