… mobiledevices. Specifically, we summarize and compare the state-of-the-art DL techniques on mobiledevices … We generalize an optimization pipeline for bringing DL to mobiledevices, …
… Besides, the excessive energy consumption from performing deeplearning models on battery-powered mobiledevices is also a severe problem to be solved. Moreover, diverse …
… device-end solution to protect mobiledevices from malware threats in real-time efficiently by leveraging customized deep … to detect malware directly on mobiledevices as a pre-installed …
S Tiku, P Kale, S Pasricha - ACM Transactions on Cyber-Physical …, 2021 - dl.acm.org
… deeplearning-based indoor localization framework that is expected to deliver accurate results on various mobiledevices … varying deeplearning model depths and across diverse mobile …
Z Mi, X Zhang, J Su, D Han, B Su - Frontiers in plant science, 2020 - frontiersin.org
… , this study proposes a novel deeplearning network C-DenseNet which … deeplearning based wheat strip rust disease grading algorithms by using color images taken by mobiledevices…
… DNN inference on mobiledevices aiming for both real-time performance and high accuracy. The difficulty of achieving real-time DNN inference on mobiledevices necessitates the …
… are intelligent sensor systems and IoTs, deeplearning in mobiledevices, deeplearning training using general-purpose GPUs, deeplearning in heterogeneous computing systems, …
… Different from the cloud and mobiledevices, microcontrollers are bare-metal devices that do not have an operating system. Therefore, we need to jointly design the deeplearning model …
E Goceri - Computers in Biology and Medicine, 2021 - Elsevier
… a mobile application for real-time image classifications because of low specifications, limited hardware resources (memory and power) and the computing ability of mobiledevices. To …