Linear conductance update improvement of CMOS-compatible second-order memristors for fast and energy-efficient training of a neural network using a memristor crossbar array
See‐On Park, Taehoon Park, Hakcheon Jeong, Seokman Hong, Seokho Seo, Yunah Kwon, Jongwon Lee, Shinhyun Choi
Abstract
-based memristor is discussed, by using a second-order memristor effect with a heating pulse and a voltage divider composed of a series resistor and two diodes. We also demonstrate that the improved device characteristics enable energy-efficient and fast training of a memristor crossbar array-based neural network with high accuracy through a realistic model-based simulation. By improving the memristor device's linearity and symmetry, our results open up the possibility of a trainable memristor crossbar array-based neural network system that possesses great energy efficiency, high area efficiency, and high accuracy at the same time.
Topics & Concepts
MemristorMemistorCrossbar switchArtificial neural networkCMOSResistive random-access memoryComputer scienceElectronic engineeringResistorLinearityTopology (electrical circuits)VoltageElectrical engineeringEngineeringArtificial intelligenceAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesNeuroscience and Neural Engineering