Design Exploration of Dynamic Multi-Level Ternary Content-Addressable Memory Using Nanoelectromechanical Relays

Taixin Li, Hongtao Zhong, Sumitha George, Vijaykrishnan Narayanan, Liang Shi, Huazhong Yang, Xueqing Li

Abstract: Multi-Level Ternary Content Addressable Memories (ML-TCAMs) are a type of TCAM that can calculate the hamming distance between the stored data and the input vector, which can be used to accelerate several specific applications. There have been several existing current-domain and charge-domain ML-TCAMs based on SRAMs and nonvolatile memories (NVMs). However, they fail to meet a good balance between area and computational accuracy tradeoffs.In this paper, for the first time, we explore the design of dynamic ML-TCAMs that achieve both high cell density and high accuracy, and propose DyLAN, the current-domain dynamic ML-TCAM using the 4-terminal nanoelectromechanical (NEM) relays. Specifically, combined with the nearly zero OFF-state leakage and stable ON-state current of the 4-terminal NEM relays, this paper proposes DyLAN-W with ultra-long retention time and DyLAN-S with ultra-low single refresh overhead and high density, respectively. Results show that DyLAN achieves up to 2.7 x and 4.9x area reduction compared with the 16T SRAM ML-TCAM and the charge-domain ML-TCAMs, respectively, and increases the few-shot learning accuracy by 13.7% (from 79.9% to 93.6%) on average compared with the state-of-the-art nonvolatile ML-TCAM, i.e., the 2FeFET ML-TCAM.