Deep neural networks (DNNs) are one of the popular models for learning relationships between complex data. Training a DNN model is a compute- and memory-intensive operation. The size of modern DNN models spans into the terabyte region, requiring multiple accelerators to train – driving up the training cost. Such humongous memory requirements shift the focus toward memory rather than computation.

CPU-memory, on the other hand, can be scaled to several terabytes with new emerging memory technologies such as HBM and CXL-attached memories. Furthermore, recent advancements to the CPUs in terms of dedicated instructions for DNN training and inference are bridging the compute gap between CPUs and accelerators.

Proposed is an exploratory work in the direction of cost-effective DNN training on CPUs where we aim to alleviate memory management challenges in DNN training. We propose TierTrain, a novel memory tiering solution based on a dynamic queuing system that leverage the periodic and deterministic memory access behavior in DNN training to manage data placement across memory tiers. TierTrain proactively manages tensors by aggressively offloading them to slow memory tiers (NVMM, CXL) and timely prefetching them back to fast memory tiers (HBM, DRAM). Our evaluation of TierTrain on a tiered memory system with a real CXL-attached memory used for memory expansion and NVMM as a low cost memory results in average fast memory footprint reduction of 59–83% and peak fast memory footprint reduction of 25–74% with a performance overhead of 1–16%. In a memory-constrained scenario, TierTrain outperforms the state-of-the-art tiering by improving the performance by 35–84% for a set of popular DNN training models.