Mobile Multimedia/Image Processing, Security, and Applications 2019 2019
DOI: 10.1117/12.2518469
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Deep learning on mobile devices: a review

Abstract: Recent breakthroughs in deep learning and artificial intelligence technologies have enabled numerous mobile applications. While traditional computation paradigms rely on mobile sensing and cloud computing, deep learning implemented on mobile devices provides several advantages. These advantages include low communication bandwidth, small cloud computing resource cost, quick response time, and improved data privacy. Research and development of deep learning on mobile and embedded devices has recently attracted m… Show more

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Cited by 83 publications
(33 citation statements)
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“…In the example (shown on the left part of Figure 11), the kernel value 1 requires the elements in the first two rows of the input matrix while value 2 requires the second and third rows. The elements in the second row [7,8,9,10] are loaded twice (from cache to register). PatDNN eliminates this load redundancy by explicitly reusing the (SIMD) registers that already hold the required data (like the second row in the above example).…”
Section: Load Redundancy Elimination (Lre)mentioning
confidence: 99%
See 1 more Smart Citation
“…In the example (shown on the left part of Figure 11), the kernel value 1 requires the elements in the first two rows of the input matrix while value 2 requires the second and third rows. The elements in the second row [7,8,9,10] are loaded twice (from cache to register). PatDNN eliminates this load redundancy by explicitly reusing the (SIMD) registers that already hold the required data (like the second row in the above example).…”
Section: Load Redundancy Elimination (Lre)mentioning
confidence: 99%
“…After obtaining DNN models trained with a huge amount of data, they can be deployed for inference, perception and control tasks in various autonomous systems and internet-of-things (IoT) applications. Recently, along arXiv:2001.00138v4 [cs.LG] 22 Jan 2020 with the rapid emergence of high-end mobile devices 1 , executing DNNs on mobile platforms gains popularity and is quickly becoming the mainstream [9,28,30,43,63] for broad applications such as sensor nodes, wireless access points, smartphones, wearable devices, video streaming, augmented reality, robotics, unmanned vehicles, smart health devices, etc. [2,3,29,46,50].…”
Section: Introductionmentioning
confidence: 99%
“…Like ER, VR are also pertinent and can be considered in future SLR studies. Other research types must be ignored in subsequent work [14]. e codominance of SP, VR, and PP to the detriment of ER reflects that the majority of proposed solutions are not implemented or experimented in real context.…”
Section: Rq2: Which Research Types Are Adopted In Selectedmentioning
confidence: 99%
“…Recently, the introduction of machine learning techniques in the creation of intelligent mobile apps is the subject of several research works of which the number keeps increasing year by year. is new orientation towards mobile applications is encouraged by the growth in performance of smartphones in terms of CPU power, RAM capacity, and energy storage, and also it is due to advances in the field of cloud computing that provide an on-demand cloud services of data storage and computing power [14]. us, to structure this research axis and to facilitate the information extraction about various available publications, it becomes imperative to carry out a literature study.…”
Section: Introductionmentioning
confidence: 99%
“…Recent years have witnessed significant progress in edge devices and wireless sensor networks, creating unprecedented opportunities to deploy deep learning and artificial intelligence (AI) technologies in IoT, while significantly adding calculation burdens in edges 2 . However, edges consisting of mobile devices and embedded systems usually have limited resources and power, especially when they are used for real-time applications, such resource and power deficiency will results in recognition and prediction accuracy loss in a learning system and even malfunctions in IoT [1][2][3][4][5][6] .…”
Section: Introductionmentioning
confidence: 99%