Hybrid Storage Class Memory-Enhanced Machine Learning Inference in Embedded Edge Devices

With the rapid proliferation of machine learning inference (MLI) tasks on Internet of Things (IoT) devices, the demand for enhanced memory system performance in these devices has become increasingly critical. This letter proposes a hybrid storage class memory (HSCM) optimization method to improve the efficiency of MLI on IoT devices. We conducted an in-depth analysis of memory access characteristics in MLI scenarios and found that the diversity and frequency of data access patterns significantly affect storage performance. Based on these characteristics, we propose a hybrid memory migration management strategy using the Markov model, which can dynamically adjust data migration between different storage levels to adapt to changing access patterns. We designed an acceleration method based on hybrid storage class memory that improves inference speed and energy efficiency by optimizing data access paths and reducing latency. Experiments demonstrate that the proposed method outperforms existing memory systems, providing a new solution for efficient MLI on embedded IoT devices.