Despite the data-rich environment in which memory systems of modern computing platforms operate, many state-of-the-art architectural policies employed in the memory system rely on static, human-designed heuristics that fail to truly adapt to the workload and system behavior via principled learning methodologies. In this article, we propose a fundamentally different design approach: using lightweight and practical machine learning (ML) methods to enable adaptive, data-driven control throughout the memory hierarchy. We present three ML-guided architectural policies: (1) Pythia, a reinforcement learning-based data prefetcher for on-chip caches, (2) Hermes, a perceptron learning-based off-chip predictor for multi-level cache hierarchies, and (3) Sibyl, a reinforcement learning-based data placement policy for hybrid storage systems. Our evaluation shows that Pythia, Hermes, and Sibyl significantly outperform the best-prior human-designed policies, while incurring modest hardware overheads. Collectively, this article demonstrates that integrating adaptive learning into memory subsystems can lead to intelligent, self-optimizing architectures that unlock performance and efficiency gains beyond what is possible with traditional human-designed approaches.

翻译：尽管现代计算平台的内存系统运行在数据丰富的环境中，但许多内存系统中采用的先进架构策略仍依赖于静态的、人工设计的启发式方法，这些方法未能通过系统的学习方法真正适应工作负载和系统行为。本文提出一种根本不同的设计方法：利用轻量级且实用的机器学习方法，在整个内存层次结构中实现自适应、数据驱动的控制。我们提出了三种机器学习引导的架构策略：（1）Pythia，一种基于强化学习的片上缓存数据预取器；（2）Hermes，一种基于感知器学习的多级缓存层次结构片外预测器；（3）Sibyl，一种基于强化学习的混合存储系统数据放置策略。评估结果表明，Pythia、Hermes和Sibyl在硬件开销适中的情况下，显著优于现有最佳的人工设计策略。总体而言，本文论证了将自适应学习集成到内存子系统中，能够催生智能、自优化的架构，从而突破传统人工设计方法的局限，实现性能与效率的双重提升。