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

Abstract

Abstract 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.

Keywords

• in‐memory search
• self‐selective memristor
• similarity search
• ternary content addressable memory