PIM-DL: Expanding the Applicability of Commodity DRAM-PIMs for Deep Learning via Algorithm-System Co-Optimization

DRAM-based processing-in-memory (DRAM-PIM) has gained commercial prominence in recent years. However, their integration for deep learning acceleration poses inherent challenges. Existing DRAM-PIMs are limited in computational capabilities, primarily applicable for element-wise and GEMV operators. Unfortunately, these operators contribute only a small portion of the execution time in most DNN workloads. Current systems still necessitate powerful hosts to handle a significant portion of compute-heavy operators.

To expand the applicability of commodity DRAM-PIMs in accelerating deep learning, we introduce a novel PIM-DL framework. The philosophy behind PIM-DL is to replace