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Dealing with Non-Idealities in Memristor Based Computation-In-Memory Designs

Anteneh Gebregiorgis, Abhairaj Singh, Sumit Diware, Rajendra Bishnoi, Said Hamdioui

202219 citationsDOIOpen Access PDF

Abstract

Computation-In-Memory (CIM) using memristor devices provides an energy-efficient hardware implementation of arithmetic and logic operations for numerous applications, such as neuromorphic computing and database query. However, memristor-based CIM suffers from various non-idealities such as conductance drift, read disturb, wire parasitics, endurance and device degradation. These negatively impact the computation accuracy of CIM. It is therefore essential to deal with these non-idealities and fabrication imperfections in order to harness the full potential of CIM. This paper discusses the non-ideality challenges and provides potential solutions. Furthermore, the paper outlines the potential future directions for CIM architectures.

Topics & Concepts

MemristorNeuromorphic engineeringComputationParasitic extractionComputer scienceComputer architectureElectronic engineeringEmbedded systemEngineeringArtificial intelligenceArtificial neural networkAlgorithmAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesNeuroscience and Neural Engineering