1 research outputs found
A Survey of Resource Management for Processing-in-Memory and Near-Memory Processing Architectures
Due to amount of data involved in emerging deep learning and big data
applications, operations related to data movement have quickly become the
bottleneck. Data-centric computing (DCC), as enabled by processing-in-memory
(PIM) and near-memory processing (NMP) paradigms, aims to accelerate these
types of applications by moving the computation closer to the data. Over the
past few years, researchers have proposed various memory architectures that
enable DCC systems, such as logic layers in 3D stacked memories or charge
sharing based bitwise operations in DRAM. However, application-specific memory
access patterns, power and thermal concerns, memory technology limitations, and
inconsistent performance gains complicate the offloading of computation in DCC
systems. Therefore, designing intelligent resource management techniques for
computation offloading is vital for leveraging the potential offered by this
new paradigm. In this article, we survey the major trends in managing PIM and
NMP-based DCC systems and provide a review of the landscape of resource
management techniques employed by system designers for such systems.
Additionally, we discuss the future challenges and opportunities in DCC
management.Comment: Accepted to appear in Journal of Low Power Electronics and
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