An associative capacitive network based on nanoscale complementary resistive switches for memory-intensive computing

Abstract

We report on the implementation of an Associative Capacitive Network (ACN) based on the nondestructive capacitive readout of two Complementary Resistive Switches (2-CRSs). ACNs are capable of performing a fully parallel search for Hamming distances (i.e. similarity) between input and stored templates. Unlike conventional associative memories where charge retention is a key function and hence, they require frequent refresh cycles, in ACNs, information is retained in a nonvolatile resistive state and normal tasks are carried out through capacitive coupling between input and output nodes. Each device consists of two CRS cells and no selective element is needed, therefore, CMOS circuitry is only required in the periphery, for addressing and read-out. Highly parallel processing, nonvolatility, wide interconnectivity and low-energy consumption are significant advantages of ACNs over conventional and emerging associative memories. These characteristics make ACNs one of the promising candidates for applications in memory-intensive and cognitive computing, switches and routers as binary and ternary Content Addressable Memories (CAMs) and intelligent data processing.