Oxide-based filamentary RRAM for deep learning
Yizhou Zhang, Peng Huang, Bin Gao, Jinfeng Kang, Huaqiang Wu
Abstract
Abstract We provide an overview of the field of oxide-based filamentary resistive random access memory (RRAM) for deep learning neural networks (DNNs). After introducing the electrical characteristics of filamentary RRAM which are the main influences on the performance of DNNs, we show several cases focusing on the optimization of the linearity and symmetry of conductance modulation and precision. Progress in the optimization of energy efficiency and reliability of DNNs is then reviewed. Lastly, our recent work on DNNs based on analog filamentary RRAM, including the design and fabrication of the device, is introduced in detail.
Topics & Concepts
Resistive random-access memoryMaterials scienceNanotechnologyArtificial intelligenceComputer scienceElectrical engineeringEngineeringVoltageAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesSemiconductor materials and devices