Hierarchical Memory Systems for AI Workloads: From Architecture to Optimization

Abstract

Memory architecture has transformed from a secondary consideration into a crucial performance determinant amid the explosive growth of artificial intelligence, especially large language models and deep neural networks. This article delves into hierarchical memory systems for AI workloads, revealing how strategically arranged memory technologies balance speed, capacity, efficiency, and cost. Spanning from lightning-fast registers to massive persistent storage, the discussion highlights specialized AI enhancements: integrated on-chip buffers, high-bandwidth memory configurations, seamless unified memory frameworks, innovative compression methods, and flexible disaggregated memory pools. These approaches boost data proximity advantages, handle ever-expanding model dimensions, slash power requirements, and maximize available bandwidth. Yet significant hurdles remain: the energy drain of data movement, bewildering programming complexity, and maintaining consistency across distributed systems. Promising horizons include blending diverse memory technologies, intelligent software-managed memory allocation, task-specific memory arrangements, and revolutionary memory-centered designs that distribute computation throughout storage layers, potentially reshaping tomorrow's AI hardware landscape.