A Complementary 3T-Based eDRAM Macro for High-Density Dual-Direction CAM and Logic-in-Memory

Content-addressable memory (CAM) is regarded as an attractive solution for data-intensive applications with high-density search demands. To further improve functional flexibility yet at a low cost, several CAM macros have been developed to support multiple bit-wise logic operations. However, conventional SRAM-based CAM designs are constrained by the large bitcell area, posing significant challenges to achieve higher density. To address this issue, we propose a complementary 3T (C3T) based embedded dynamic random access memory (eDRAM) macro for high-density dual-direction CAM searching and logic-in-memory operations. First, we propose a compact C3T bitcell featuring a pair of complementary decoupled read ports, enabling dual-port read and efficient CAM operations. Second, we present a compact dynamic-circuit-based sense amplifier (DSA) to optimize the area of readout peripheral circuitry while mitigating the read bit line saturation issue. Additionally, we implement dual-direction CAM searching and logic-in-memory operations exploiting the C3T-based eDRAM macro. A 4 Kb C3T-based eDRAM macro has been validated in a commercial 40-nm CMOS process. Post-layout results demonstrate a 53% reduction in the bitcell area and a 58.1% reduction in the macro area compared to the state-of-the-art 6T compute SRAM. Moreover, the proposed design achieves a maximum frequency of 578 MHz for binary CAM (BCAM) searching operations and 694 MHz for logic operations, with energy consumption of 1.12 fJ/bit and 26.8 fJ/bit, respectively.

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Publication Details

Journal
IEEE Transactions on Circuits and Systems I Regular Papers
Published
2025-09-16
DOI
https://doi.org/10.1109/tcsi.2025.3609463
Primary Topic
Advanced Memory and Neural Computing
Type
article
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article

A Complementary 3T-Based eDRAM Macro for High-Density Dual-Direction CAM and Logic-in-Memory

Hui Wang, Yuhao Shu, Lintao Lan, Yifei Li et al.
IEEE Transactions on Circuits and Systems I Regular Papers
Advanced Memory and Neural Computing
article

A Complementary 3T-Based eDRAM Macro for High-Density Dual-Direction CAM and Logic-in-Memory

Hui Wang, Yuhao Shu, Lintao Lan, Yifei Li, Baiqi Ning, Yajun Ha, Yuxin Zhou, Jincheng Wang
article en

Abstract

Content-addressable memory (CAM) is regarded as an attractive solution for data-intensive applications with high-density search demands. To further improve functional flexibility yet at a low cost, several CAM macros have been developed to support multiple bit-wise logic operations. However, conventional SRAM-based CAM designs are constrained by the large bitcell area, posing significant challenges to achieve higher density. To address this issue, we propose a complementary 3T (C3T) based embedded dynamic random access memory (eDRAM) macro for high-density dual-direction CAM searching and logic-in-memory operations. First, we propose a compact C3T bitcell featuring a pair of complementary decoupled read ports, enabling dual-port read and efficient CAM operations. Second, we present a compact dynamic-circuit-based sense amplifier (DSA) to optimize the area of readout peripheral circuitry while mitigating the read bit line saturation issue. Additionally, we implement dual-direction CAM searching and logic-in-memory operations exploiting the C3T-based eDRAM macro. A 4 Kb C3T-based eDRAM macro has been validated in a commercial 40-nm CMOS process. Post-layout results demonstrate a 53% reduction in the bitcell area and a 58.1% reduction in the macro area compared to the state-of-the-art 6T compute SRAM. Moreover, the proposed design achieves a maximum frequency of 578 MHz for binary CAM (BCAM) searching operations and 694 MHz for logic operations, with energy consumption of 1.12 fJ/bit and 26.8 fJ/bit, respectively.

IEEE Transactions on Circuits and Systems I Regular PapersVol. 73(2)
ShanghaiTech University (CN), Shanghai Advanced Research Institute (CN), Nanjing University of Aeronautics and Astronautics (CN)
Openalex Percentile: Top 76%
Advanced Memory and Neural Computing
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