2026-05-31T12:16:51Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/380412026-02-03T11:11:49Zcom_10630_2254col_10630_37953

Enabling Easier Programming of Machine Learning Algorithms on Robots with oneAPI Toolkits. Constantinescu, Denisa-Andreea González-Navarro, María Ángeles Asenjo-Plaza, Rafael Fernández-Madrigal, Juan Antonio Cruz-Martín, Ana María Aprendizaje automático Aplicaciones informáticas - Desarrollo This work shows that it is feasible to solve large-scale decision-making problems for robot navigation in real-time onboard low-power heterogeneous CPU+iGPU platforms. We can achieve both performance and productivity by carefully selecting the scheduling strategy and programming model. In particular, we remark that the oneAPI programming model creates new opportunities to improve productivity, performance, and efficiency in low-power systems. Our experimental results show that the implementations based on the oneAPI programming model are up to 5× easier to program than those based on OpenCL while incurring only 3 to 8% overhead for low-power systems. 2025-02-27T10:22:00Z 2025-02-27T10:22:00Z 2022 journal article https://r6.ieee.org/scv-cs/wp-content/uploads/sites/81/2022/03/1-CS-Mag-Feedforward-Denisa-MyFinal.pdf https://r6.ieee.org/scv-cs/magazines/ https://hdl.handle.net/10630/38041 eng http://creativecommons.org/licenses/by/4.0/ open access Attribution 4.0 Internacional SCV Chapter, IEEE Computer Society