Nonvolatile memory (NVM)-based computing in-memory (CIM) is a promising solution to data-intensive applications. This work proposes a 2T2R resistive random access memory (ReRAM) architecture that supports three types of CIM operations: 1) ternary content addressable memory (TCAM); 2) logic in-memory (LiM) primitives and arithmetic blocks such as full adder (FA) and full subtractor; and 3) in-memory dot-product for neural networks. The proposed architecture allows the NVM operations in both 2T2R and conventional 1T1R configurations. The proposed LiM full adder (LiM-FA) improves the delay, the static power, and the dynamic power by $3.2\times $ , $1.2\times $ , and $1.6\times $ , respectively, compared with state-of-the-art LiM-FAs. Furthermore, based on different optimization techniques and robustness analysis, a lower precharge voltage is set for each mode. This reduces the TCAM search energy and 1T1R ReRAM access energy by $1.6\times $ and $1.14\times $ , respectively, compared with the case without optimizations.

中文翻译：

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用于多功能数据存储和内存计算的可重构 2T2R ReRAM 架构

基于非易失性存储器 (NVM) 的内存计算 (CIM) 是数据密集型应用程序的有前途的解决方案。这项工作提出了一种 2T2R 电阻式随机存取存储器 (ReRAM) 架构，该架构支持三种类型的 CIM 操作：1) 三元内容可寻址存储器 (TCAM)；2) 内存中的逻辑 (LiM) 原语和算术块，例如全加器 (FA) 和全减法器；和 3) 用于神经网络的内存中点积。所提出的架构允许在 2T2R 和传统的 1T1R 配置中进行 NVM 操作。建议的 LiM 全加器 (LiM-FA) 将延迟、静态功耗和动态功耗提高了 $3.2\times $ , $1.2\times $ ， 和 $1.6\times $ ，分别与最先进的 LiM-FA 进行比较。此外，基于不同的优化技术和稳健性分析，为每种模式设置了较低的预充电电压。这减少了 TCAM 搜索能量和 1T1R ReRAM 访问能量 $1.6\times $ 和 $1.14\times $ ，分别与没有优化的情况相比。