Design of High-Performance and Compact CAM for Supporting Data-Intensive Applications
Liu Liu, Ann Franchesca Laguna, Michael Thaddeus Niemier, Xiaobo Sharon Hu · 2024
Content addressable memory (CAM) is a special-purpose search engine that can support parallel search directly in memory. CAMs are of increasing interest for machine learning and data analytics applications that require intensive search operations. However, conventional CMOS CAMs have large cell areas and high energy consumption, which limits applicability. Also, many data-intensive applications need more efficient data representation and approximate matching functions, which may not be efficiently realized by conventional ternary CAMs. As such, we introduce a more compact and high-performance CAM design based on non-volatile ferroelectic FET devices. Furthermore, we present a reconfigurable CAM design, MHCAM, to support approximate search for multi-dimensional data. We use DNA alignment as a proxy application to illustrate the design’s application-level benefits.