개인용 모바일 환경의 AI 워크로드 수행을 위한 메모리 참조 분석 및 시스템 설계 방안 = Implications for Memory Reference Analysis and System Design to Execute AI Workloads in Personal Mobile Environments

다국어 초록 (Multilingual Abstract)

Recently, mobile apps that utilize AI technologies are increasing. In the personal mobile environment, performance degradation may occur during the training phase of large AI workload due to limitations in memory capacity. In this paper, we extract memory reference traces of AI workloads and analyze their characteristics. From this analysis, we observe that AI workloads can cause frequent storage access due to weak temporal locality and irregular popularity bias during memory write operations, which can degrade the performance of mobile devices. Based on this observation, we discuss ways to efficiently manage memory write operations of AI workloads using persistent memory-based swap devices. Through simulation experiments, we show that the system architecture proposed in this paper can improve the I/O time of mobile systems by more than 80%.