LAUSR

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.