Resistive-RAM-Based In-Memory Computing for Neural Network: A Review
Weijian Chen, Zhi Qi, Zahid Akhtar, Kamran Siddique
Abstract
Processing-in-memory (PIM) is a promising architecture to design various types of neural network accelerators as it ensures the efficiency of computation together with Resistive Random Access Memory (ReRAM). ReRAM has now become a promising solution to enhance computing efficiency due to its crossbar structure. In this paper, a ReRAM-based PIM neural network accelerator is addressed, and different kinds of methods and designs of various schemes are discussed. Various models and architectures implemented for a neural network accelerator are determined for research trends. Further, the limitations or challenges of ReRAM in a neural network are also addressed in this review.
Topics & Concepts
Resistive random-access memoryCrossbar switchComputer scienceArtificial neural networkMemristorIn-Memory ProcessingComputer architectureResistive touchscreenRandom access memoryComputationComputer hardwareArtificial intelligenceElectronic engineeringEngineeringElectrical engineeringVoltageOperating systemTelecommunicationsAlgorithmQuery by ExampleInformation retrievalWeb search querySearch engineAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesNeuroscience and Neural Engineering