Near memory data structure rearrangement

Abstract

As CPU core counts continue to increase, the gap between compute power and available memory bandwidth has widened. A larger and deeper cache hierarchy benefits locality-friendly computation, but offers limited improvement to irregular, data intensive applications. In this work we explore a novel approach to accelerating these applications through in-memory data restructuring. Unlike other proposed processing-in-memory architectures, the rearrangement hardware performs data reduction, not compute offload. Using a custom FPGA emulator, we quantitatively evaluate performance and energy benefits of near-memory hardware structures that dynamically restructure in-memory data to cache-friendly layout, minimizing wasted memory bandwidth. Our results on representative irregular benchmarks using the Micron Hybrid Memory Cube memory model show speedup, bandwidth savings, and energy reduction. We present an API for the near-memory accelerator and describe the interaction between the CPU and the rearrangement hardware with application examples. The merits of an SRAM vs. a DRAM scratchpad buffer for rearranged data are explored.