Processing-in-Memory (PIM) has evolved over decades into a feasible solution to addressing the exacerbating performance bottleneck with main memory by placing computational logic in or near memory.
Recent proposals from DRAM manufacturers highlighted the HW constraint-aware design of PIM-enabled DRAM with specialized MAC logic, providing an order of magnitude speedup for memory-intensive operations in DL models.
Although the main target for PIM acceleration did not initially include convolutional neural networks due to their high compute intensity, recent CNN models are increasingly adopting computationally lightweight implementation.
Motivated by the potential for the software stack to enable CNN models on DRAM-PIM hardware without invasive changes, we propose PIMFlow, an end-to-end compiler and runtime support, to accelerate CNN models on a PIM-enabled GPU memory.
PIMFlow transforms model graphs to create inter-node parallelism across GPU and PIM, explores possible task- and data-parallel execution scenarios for optimal execution time, and provides a code-generating back-end and execution engine for DRAM-PIM.
PIMFlow achieves up to 82% end-to-end speedup and reduces energy consumption by 26% on average for CNN model inferences.