Reconfigurable 2T2R ReRAM architecture for versatile data storage and computing in-memory

Abstract

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×, 1.2×, and 1.6×, 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× and 1.14×, respectively, compared with the case without optimizations.