TransMigrator: A Transformer-Based Predictive Page Migration Mechanism for Heterogeneous Memory.

Page migration strategies are crucial to the performance of a hybrid main memory system which consists of DRAM and Non-Volatile RAM. Previous locality-based migration strategies have limitations on deciding which pages should be placed in limited DRAM. In this paper, we propose TransMigrator, a transformer-based predictive page migration mechanism. TransMigrator uses an end-to-end neural network to directly predict the page that will be accessed most in the near future, by learning patterns from long memory access history. The network achieved 0.7245 average accuracy of prediction with 0.804 MB model parameter size. Besides, a threshold-based method is used at the same time to make the system robust. TransMigrator reduces access time by 23.59% on average compared with AC-CLOCK, THMigrator and VC-HMM.