Scalability of the ARM Nvidia Grace Superchip for Deep Learning Applications - Araújo, Thiago; Navaux, Philippe (Anais da Escola Regional de Alto Desempenho da Região Sul (ERAD-RS), 2025)
The advance of Artificial Intelligence (AI) has heightened the demand for computational resources, presenting challenges in scalability and energy efficiency. The low-power ARM architecture is a promising option for AI workloads. This study assesses the scalability of the ARM Nvidia Grace Superchip in Deep Learning (DL) using a model that predicts referrals for severe diabetic retinopathy. We analyze performance in terms of speedup and energy consumption across 1 to 144 cores. Although speedup increases with more threads, the gains are less pronounced at higher core counts due to resource saturation. Energy consumption, however, significantly decreases, showing a 95% reduction when utilizing all cores. These results emphasize the Superchip’s potential for scalable and energy-efficient AI, especially in demanding applications. Future research will focus on memory usage, latency, and distributed learning.