Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases

Today's computing systems require moving data back-and-forth between computing resources (e.g., CPUs, GPUs, accelerators) and off-chip main memory so that computation can take place on the data. Unfortunately, this data movement is a major bottleneck for system performance and energy consumption [1], [2]. One promising execution paradigm that alleviates the data movement bottleneck in modern and emerging applications is processing-in-memory (PIM) [2]–[12], where the cost of data movement to/from main memory is reduced by placing computation capabilities close to memory. In the data-centric PIM paradigm, the logic close to memory has access to data with significantly higher memory bandwidth, lower latency, and lower energy consumption than processors/accelerators in existing processor-centric systems.

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