用模糊神经网络提升微架构设计的可解释性与效率
Explainable Fuzzy Neural Network with Multi-Fidelity Reinforcement Learning for Micro-Architecture Design Space Exploration
- 结合模糊神经网络提取设计决策知识
- 多保真强化学习仅需少量高成本数据即可高效探索
- 适合需要可解释性的芯片设计团队使用
随着处理器技术不断发展，现代微架构设计日益复杂，庞大的设计空间给人工设计带来巨大挑战，使设计空间探索（DSE）算法成为μ-架构设计的关键工具。近年来，尽管已有基于贝叶斯优化和集成学习等方法取得良好成果，但这些方法普遍缺乏可解释性，阻碍了设计者对决策过程的理解。为此，本文提出利用模糊神经网络（FNN）从DSE过程中归纳并总结知识，增强决策的可解释性与可控性。同时，引入多保真强化学习（MFRL）框架，优先使用低成本但精度较低的数据进行探索，大幅减少对高成本数据的依赖。实验表明，该方法在极小样本预算下仍能取得优异性能，并超越当前最优方法。所提出的DSE框架已开源，地址为 https://github.com/fanhanwei/FNN_MFRL_ArchDSE/。
原文摘要 · Abstract (English)
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 $μ$-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/\ .
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