FACAM: Design and Optimization of A Compact Energy Efficient FeFET-Based Analog Content Addressable Memory

Content Addressable Memory (CAM) is known for highly parallel pattern matching capability, which is widely used for data-centric applications and advanced machine learning models that involve associative search tasks. However, most state-of-the-art CAM designs focus on binary/multi-bit CAMs (B/MCAMs) based on CMOS or emerging nonvolatile memories (NVMs), which struggle in scenarios where analog values, rather than discrete levels, need to be stored and searched. Therefore, analog CAMs (ACAMs) offer a promising solution to further increase memory density, improve energy efficiency and extend practical scenarios. Among NVMs, ferroelectric field effect transistors (FeFETs) have emerged as a strong candidate for efficient CAM designs due to the three-terminal structure, high on-off ratio, high OFF resistance and voltage-driven write/read mechanisms. In this paper, we propose FACAM, a compact and energy efficient single-input 2FeFET-1T ACAM cell design, with a two-phase search scheme, that sets the location and width of the matching range through two FeFETs, respectively. We further present a FACAM array which reduces the matchline (ML) voltage swing by shifting ML precharging into the in-cell search operations. We also propose an adaptive scheme to selectively early-terminate second search phase for further search energy optimization. Evaluation results suggest that our proposed FACAM achieves 8.39× and 2.94× energy efficiency compared with the state-of-the-art better 6T-2R ACAM and 2FeFET ACAM. Benchmarking results in deep random forest accelerator show that our approach is 2.14× faster and 7.82× energy efficient than 2FeFET ACAM.