#ALL BENCHMARK AI SMARTPHONE ANDROID#

While standard smartphone apps are no longer a problem for them, there is still a group of tasks that can easily challenge even high-end devices, namely running artificial intelligence algorithms. Over the last years, the computational power of mobile devices such as smartphones and tablets has grown dramatically, reaching the level of desktop computers available not long ago.

#ALL BENCHMARK AI SMARTPHONE ANDROID#

All numerical results provided in this paper can be found and are regularly updated on the official project website.ДЄI Benchmark: Running Deep Neural Networks on Android SmartphonesДЄndrey Ignatov, Radu Timofte, William Chou, Ke Wang, Max Wu, Tim Hartley, Luc Van Gool We also discuss the recent changes in the Android ML pipeline and provide an overview of the deployment of deep learning models on mobile devices. In this paper, we evaluate the performance and compare the results of all chipsets from Qualcomm, HiSilicon, Samsung, MediaTek and Unisoc that are providing hardware acceleration for AI inference. The current 4th generation of mobile NPUs is already approaching the results of CUDA-compatible Nvidia graphics cards presented not long ago, which together with the increased capabilities of mobile deep learning frameworks makes it possible to run complex and deep AI models on mobile devices. The performance of mobile AI accelerators has been evolving rapidly in the past two years, nearly doubling with each new generation of SoCs.

AI Benchmark: All About Deep Learning on Smartphones in 2019ДЄndrey Ignatov, Radu Timofte, Andrei Kulik, Seungsoo Yang, Ke Wang, Felix Baum, Max Wu, Lirong Xu, Luc Van Gool

All benchmark ai smartphone