Advancements in Content-Addressable Memory (CAM) Circuits: State-of-the-Art, Applications, and Future Directions in the AI Domain

被引：0

作者：

Molom-Ochir, Tergel ^{[1
]}

Taylor, Brady ^{[1
]}

Li, Hai ^{[1
]}

Chen, Yiran ^{[1
]}

机构：

[1] Duke Univ, Dept Elect & Comp Engn, Durham, NC 27705 USA

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS | 2025年

基金：

美国国家科学基金会;

关键词：

Associative memory; Machine Learning; in-memory computing; semiconductor devices; digital circuits; analog circuits; NONVOLATILE TCAM; ERROR-DETECTION; TERNARY CAM; SEARCH-TIME; RERAM; CELL; MATCHLINE;

D O I：

10.1109/TCSI.2025.3527309

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Content-Addressable Memory (CAM) circuits, distinguished by their ability to accelerate data retrieval through a direct content-matching function, are increasingly crucial in the era of AI and increasing data computation. With the rise of AI models, hardware matching and hashing capabilities become essential, underscoring the need for a comprehensive survey of this evolving technology. This survey explores various CAM types across circuit designs and technologies, highlighting contributions to fields such as Machine Learning and genomics. We review 37 CAM cell designs, focusing on emerging trends in area and energy efficiency, pivotal for next-generation computing. Furthermore, we discuss current challenges and suggest future research directions in CAM technology.

引用

页数：12

共 24 条

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