Characterizing Memory Access Patterns of Various Convolutional Neural Networks for Utilizing Processing-in-Memory
Jihoon Jang, Hyun Kim, Hyokeun Lee · 2023
Convolutional neural network (CNN) models require deeper networks and more training data for better performance, which in turn results in greater computational and memory requirements. In this paper, we analyze the memory access patterns that occur in main memory during the training processes of various CNN models. CNN training is a linear procedure consisting of a forward pass (FP) and a backward pass (BP). As a result of the analysis, we found that BP accounted for 83.4% of the total main memory accesses on average. Therefore, CNN training including FP and BP is much more memory-intensive than CNN inference using only FP. This demonstrates that CNN training is a suitable application for near-data processing to reduce memory bottlenecks and conserve computational resources.