Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Thiago Araújo, Philippe Navaux
Institution: Universidade Federal do Rio Grande do Sul
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funders: Fundação de Amparo à Pesquisa do Estado de São Paulo, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Ministério da Ciência, Tecnologia e Inovação, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, Ministério da Ciência, Tecnologia, Inovações e Comunicações
OpenAlex ID: W4411169772