Machine Learning Techniques for Understanding and Predicting Memory Interference in CPU-GPU Embedded Systems

Nowadays, heterogeneous embedded platforms are extensively used in various low-latency applications, including the automotive industry, real-time IoT systems, and automated factories. These platforms utilize specific components, such as CPUs, GPUs, and neural network accelerators for efficient task processing and to solve specific problems with a lower power consumption compared to more traditional systems. However, since these accelerators share resources such as the global memory, it is crucial to understand how workloads behave under high computational loads to determine how parallel computational engines on modern platforms can interfere and adversely affect the system's predictability and performance. One area that remains unclear is the interference effect on shared memory resources between the CPU and GPU: more specifically, the latency degradation experienced by GPU kernels when memory-intensive CPU applications run concurrently. In this work, we first analyze the metrics that characterize the behavior of different kernels under various board conditions caused by CPU memory-intensive workloads on a Nvidia Jetson Xavier. Then, we exploit various machine learning methodologies aiming to estimate the latency degradation of kernels based on their metrics. As a result of this, we are able to identify the metrics that could potentially have the most significant impact when predicting the kernels completion latency degradation.