Self‐selective memristor‐enabled in‐memory search for highly efficient data mining

Similarity search, that is, finding similar items in massive data, is a fundamental computing problem in many fields such as data mining and information retrieval. However, for large‐scale and high‐dimension data, it suffers from high computational complexity, requiring tremendous computation resources. Here, based on the low‐power self‐selective memristors, for the first time, we propose an in‐memory search (IMS) system with two innovative designs. First, by exploiting the natural distribution law of the devices resistance, a hardware locality sensitive hashing encoder has been designed to transform the real‐valued vectors into more efficient binary codes. Second, a compact memristive ternary content addressable memory is developed to calculate the Hamming distances between the binary codes in parallel. Our IMS system demonstrated a 168× energy efficiency improvement over all‐transistors counterparts in clustering and classification tasks, while achieving a software‐comparable accuracy, thus providing a low‐complexity and low‐power solution for in‐memory data mining applications. An in‐memory search system is demonstrated with the self‐selective memristor. It consists of a hardware locality sensitive hashing encoder and a highly compact ternary content addressable memory (TCAM). By exploiting the intrinsic randomness of device and the natural parallelism of the crossbar array in matrix multiplication and search, the system achieves 168 times energy saving in data mining comparing to the all‐complementary metal oxide semiconductor (CMOS) implementation.