用轻量机器学习让内存系统自动适应工作负载，提升性能与效率。
Machine Learning-Driven Intelligent Memory System Design: From On-Chip Caches to Storage
- 采用强化学习与感知机算法，实现内存层级的自适应控制。
- 三个新策略在多级缓存和混合存储中均超越传统人工设计方法。
- 适合关注智能硬件优化与系统性能提升的研究者与工程师。
现代计算平台的内存系统面临数据密集环境，但现有许多架构策略仍依赖静态的人工启发式规则，无法通过系统化学习方法真正适应工作负载与系统行为。本文提出一种根本性设计思路：利用轻量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。我们提出了三种基于机器学习的架构策略：(1) Pythia，一种基于强化学习的片上缓存数据预取器；(2) Hermes，一种基于感知机学习的片外多级缓存预测器；(3) Sibyl，一种基于强化学习的混合存储数据放置策略。评估结果表明，Pythia、Hermes 和 Sibyl 显著优于现有最佳的人工设计策略，且硬件开销可控。本研究证明，将自适应学习融入内存子系统可构建智能、自我优化的架构，实现传统人工设计无法达到的性能与效率提升。
原文摘要 · Abstract (English)
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.
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