Memristor-based neuromorphic system with content addressable memory structure

Abstract

By mimicking the complex biological systems, neuromorphic system is more efficient and less energy-efficient than the traditional Von Neumann architecture. Due to the similarity between memristor and biological synapse, many research efforts have been investigated in utilizing the latest discovered memristor as synapse. This paper improves the original network circuit based on memristor and content addressable memory structure and extends the existing results in the literature. The competition network circuit includes input layer, synapse and output layer. The synapse is made up of two memristors which store information and judge whether input and storage data are same. The output layer consists of subtractor which processes match and mismatch voltage to recognize pattern and the winner-take-all circuit to find out of which storage pattern is the closest to input pattern. The circuit design about read/write framework and working principle are discussed in detail. Finally, the system has been trained and recognizes these 5 × 6 pixel digit images from 0 to 9 successfully.