In-Memory Computing Accelerators for Emerging Learning Paradigms

Over the past decades, emerging, data-driven machine learning (ML) paradigms have increased in popularity, and revolutionized many application domains. To date, a substantial effort has been devoted to devising mechanisms for facilitating the deployment and near ubiquitous use of these memory intensive ML models. This review paper presents the use of in-memory computing (IMC) accelerators for emerging ML paradigms from a bottom-up perspective through the choice of devices, the design of circuits/architectures, to the application-level results.