发表机构
Forschungszentrum Jülich; RWTH Aachen University; The University of Hong Kong（于利希研究中心; 亚琛工业大学; 香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出内容可寻址存储器（CAMs）作为AI中关联检索的互补硬件原语，支持并行内存匹配，并展望分层搜索、异构集成等方向以补充线性代数计算。
AI 中文摘要
现代人工智能主要在针对密集线性代数优化的计算架构上执行。虽然这推动了当代神经网络的成功，并催生了计算内存（CIM）架构，但日益增多的一类人工智能（AI）工作负载依赖于关联检索，即通过内容或相似性而非显式内存地址来识别存储信息。此类操作是Transformer注意力机制、基于树的推理、基因组搜索及其他检索密集型应用的核心，然而传统内存系统对此支持效率低下。在本观点文章中，我们认为内容可寻址存储器（CAMs）为AI中的关联处理提供了一种互补的硬件原语。我们回顾了其执行大规模并行内存匹配的能力，讨论了新兴内存技术如何提高密度和能效，并指出分层搜索、应用特定架构、硬件感知学习以及与CIM的异构集成是可扩展关联计算的关键方向。这些发展共同表明，关联检索应作为未来AI系统的基础计算原语，与线性代数相辅相成。
英文摘要
Modern artificial intelligence is predominantly executed on computing architectures optimized for dense linear algebra. While this has enabled the success of contemporary neural networks and motivated compute-in-memory (CIM) architectures, a growing class of artificial intelligence (AI) workloads depends on associative retrieval, identifying stored information by content or similarity rather than by explicit memory addresses. Such operations are central to transformer attention, tree-based inference, genomic search, and other retrieval-intensive applications, yet remain inefficiently supported by conventional memory systems. In this Perspective, we argue that content-addressable memories (CAMs) provide a complementary hardware primitive for associative processing in AI. We review their ability to perform massively parallel in-memory matching, discuss how emerging memory technologies can improve density and energy efficiency, and identify hierarchical search, application-specific architectures, hardware-aware learning, and heterogeneous integration with CIM as key directions for scalable associative computing. Together, these developments suggest that associative retrieval should complement linear algebra as a foundational computing primitive for future AI systems.
Comments30 pages, 8 figures, 3 boxes. Perspective article, submitted to Nature Communications
