Mitigating the Memory Bottleneck with Machine Learning-Driven and Data-Aware Microarchitectural Techniques

2026-03-08

AI Summary

This work addresses the growing challenge posed by the expanding data footprint of modern applications, which renders memory systems a critical bottleneck for both performance and energy efficiency—constraints that traditional microarchitectures struggle to overcome. To this end, the paper introduces a data-driven microarchitectural design paradigm that systematically integrates lightweight machine learning with application-specific data semantic features across multiple processor components. Key contributions include a reinforcement learning–based hardware prefetcher, a perceptron-driven off-chip access predictor, a synergistic mechanism coordinating prefetching and prediction, and a predictability-aware memory access elimination technique leveraging both address and value repetition. Experimental results demonstrate that the proposed approach substantially outperforms state-of-the-art solutions, delivering significant improvements in both performance and energy efficiency.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Machine learning and data science for the Web

Abstract

Modern applications process massive data volumes that overwhelm the storage and retrieval capabilities of memory systems, making memory the primary performance and energy-efficiency bottleneck of computing systems. Although many microarchitectural techniques attempt to hide or tolerate long memory access latency, rapidly growing data footprints continue to outpace technology scaling, requiring more effective solutions. This dissertation shows that modern processors observe large amounts of application and system data during execution, yet many microarchitectural mechanisms make decisions largely independent of this information. Through four case studies, we demonstrate that such data-agnostic design leads to substantial missed opportunities for improving performance and energy efficiency. To address this limitation, this dissertation advocates shifting microarchitecture design from data-agnostic to data-informed. We propose mechanisms that (1) learn policies from observed execution behavior (data-driven design) and (2) exploit semantic characteristics of application data (data-aware design). We apply lightweight machine learning techniques and previously underexplored data characteristics across four processor components: a reinforcement learning-based hardware data prefetcher that learns memory access patterns online; a perceptron predictor that identifies memory requests likely to access off-chip memory; a reinforcement learning mechanism that coordinates data prefetching and off-chip prediction; and a mechanism that exploits repeatability in memory addresses and loaded values to eliminate predictable load instructions. Our extensive evaluation shows that the proposed techniques significantly improve performance and energy efficiency compared to prior state-of-the-art approaches.

Problem

• memory bottleneck
• microarchitectural techniques
• data-aware design
• performance
• energy efficiency

Innovation

• data-aware microarchitecture
• machine learning-driven prefetching
• reinforcement learning in hardware
• perceptron-based memory prediction
• load instruction elimination