Methodologies, Workloads, and Tools for Processing-in-Memory: Enabling the Adoption of Data-Centric Architectures

The increasing prevalence and growing size of data in modern applications have led to high costs for computation in tra-ditional processor-centric computing systems. Moving large volumes of data between memory devices (e.g., DRAM) and computing elements (e.g., CPUs, GPUs) across bandwidth-limited memory channels can consume more than 60% of the total energy in modern systems [1], [2]. To mitigate these costs, the processing-in-memory (PIM) [1, 3–9] paradigm moves computation closer to where the data resides, reducing (and in some cases eliminating) the need to move data between memory and the processor.