A general-purpose in-memory computing architecture that addresses several key fundamental limitations of state-of-the-art reconfigurable data-flow architectures in supporting emerging machine learning and big data applications
Driven by recent advances in resistive random-access memory (RRAM), there have been growing interests in exploring alternative computing concept, i.e., in-memory processing, to address the classical von Neumann bottlenecks. Despite of their great …