Artificial Intelligence on the edge is a matter of great importance towards the enhancement of smart devices that rely on operations with real-time constraints. We present PolimiDL, a framework for the acceleration of Deep Learning on mobile and embedded systems with limited resources and heterogeneous architectures. Experimental results show competitive results with respect to TensorFlow Lite for the execution of small models.
CITATION STYLE
Frajberg, D., Bernaschina, C., Marone, C., & Fraternali, P. (2019). Accelerating deep learning inference on mobile systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11516 LNCS, pp. 118–134). Springer Verlag. https://doi.org/10.1007/978-3-030-23367-9_9
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