Ferroelectric Ternary Content Addressable Memories for Energy-Efficient Associative Search
Ferroelectric Ternary Content Addressable Memories for Energy-Efficient Associative Search
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DOI：
10.1109/tcad.2022.3197694
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发表时间：
2023-04
影响因子：
2.9
通讯作者：
Xunzhao Yin;Yu Qian;M. Imani;K. Ni;Chao Li;Grace Li Zhang;Bing Li;Ulf Schlichtmann;Cheng Zhuo
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文献类型：
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作者：
Xunzhao Yin;Yu Qian;M. Imani;K. Ni;Chao Li;Grace Li Zhang;Bing Li;Ulf Schlichtmann;Cheng Zhuo
在数据库中实现快速高效的搜索功能一直是机器学习、物联网应用和推理中许多数据密集型任务的核心组件。然而，在冯·诺依曼架构中，由于存储单元和处理单元之间存在大量数据传输，通过重复算术运算实现搜索功能的传统数字机器存在能效低和性能下降的问题。三态内容可寻址存储器（TCAM）是内存计算（CiM）设计的一种重要硬件形式，其目的是通过在存储块内实现并行关联搜索功能来克服数据传输瓶颈。虽然大多数最先进的TCAM设计侧重于通过利用紧凑的非易失性存储器（NVM）来提高信息密度，但在优化基于NVM的TCAM的能效方面所做的努力却很少。在本文中，我们以铁电场效应晶体管（FeFET）作为一种具有代表性的NVM，提出了一种或非型2FeFET - 1T和一种与非型2FeFET - 2T的TCAM设计，通过减少相关的预充电开销实现了高能效的关联搜索。然后我们提出了一种混合铁电与非 - 或非（HFNN）TCAM设计，以进一步提高能效。我们还提出了一种基于HFNN的分段架构，通过搜索操作流水线来减少搜索延迟和能耗。评估结果表明，所提出的2FeFET - 1T、2FeFET - 2T和HFNN TCAM设计的搜索能耗分别比传统的16T互补金属氧化物半导体（CMOS）TCAM低3.03倍、8.08倍和226.92倍。应用基准测试表明，与传统的GPU相比，我们提出的2FeFET - 1T/2FeFET - 2T/HFNN TCAM平均可节省45.2%/50.6%/57.5%的GPU能耗。
A fast and efficient search function across the database has been a core component for a number of data-intensive tasks in machine learning, IoT applications, and inference. However, the conventional digital machines implementing the search functionality with repetitive arithmetic operations suffer from the energy efficiency and performance degradation due to the significant data transfer between the storage and processing units in the Von Neumann architecture. Ternary content addressable memories (TCAMs) are an essential hardware form of computing-in-memory (CiM) designs that aim to overcome the data transfer bottlenecks by implementing the parallel associative search function within the memory blocks. While most state-of-the-art TCAM designs focus on improving the information density by harnessing compact nonvolatile memories (NVMs), little efforts have been spent on optimizing the energy efficiency of the NVM-based TCAM. In this article, by exploiting the ferroelectric FET (FeFET) as a representative NVM, we propose an NOR-type 2FeFET-1T and an NAND-type 2FeFET-2T TCAM designs that enable highly energy-efficient associative search by reducing the associated precharge overheads. We then propose a hybrid ferroelectric NAND-NOR (HFNN) TCAM design to further improve the energy efficiency. An HFNN-based segmented architecture is proposed to reduce the search delay and energy by search operation pipeline. Evaluation results suggest that the proposed 2FeFET-1T, 2FeFET-2T and HFNN TCAM design consume $3.03\times $ , $8.08\times $ , and $226.92\times $ less search energy than the conventional 16T complementary metal oxide semiconductor (CMOS) TCAM, respectively. Application benchmarking shows that our proposed 2FeFET-1T/2FeFET-2T/HFNN TCAM can save, on average, 45.2%/50.6%/57.5% the GPU energy consumption as compared to the conventional GPU.