An Energy-Efficient and Fast Scheme for Hybrid Storage Class Memory in an AIoT Terminal System

Abstract

Conventional main memory can no longer meet the requirements of low energy consumption and massive data storage in an artificial intelligence Internet of Things (AIoT) system. Moreover, the efficiency is decreased due to the swapping of data between the main memory and storage. This paper presents a hybrid storage class memory system to reduce the energy consumption and optimize IO performance. Phase change memory (PCM) brings the advantages of low static power and a large capacity to a hybrid memory system. In order to avoid the impact of poor write performance in PCM, a migration scheme implemented in the memory controller is proposed. By counting the write times and row buffer miss times in PCM simultaneously, the write-intensive data can be selected and migrated from PCM to dynamic random-access memory (DRAM) efficiently, which improves the performance of hybrid storage class memory. In addition, a fast mode with a tmpfs-based, in-memory file system is applied to hybrid storage class memory to reduce the number of data movements between memory and external storage. Experimental results show that the proposed system can reduce energy consumption by 46.2% on average compared with the traditional DRAM-only system. The fast mode increases the IO performance of the system by more than 30 times compared with the common ext3 file system.