FitCNN: A cloud-assisted lightweight convolutional neural network framework for mobile devices

Li, Shiming; Liu, Duo; Xiang, Chaoneng; Liu, Jianfeng; Ling, Yingjian; Liao, Tianjun; Liang, Liang

doi:10.1109/rtcsa.2017.8046337

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…In this section, we look at how to tailor deep learning to mobile networking applications from three perspectives, namely, mobile devices and systems, distributed data centers, and changing mobile network environments. [513] Filter size shrinking, reducing input channels and late downsampling CNN Howard et al [514] Depth-wise separable convolution CNN Zhang et al [515] Point-wise group convolution and channel shuffle CNN Zhang et al [516] Tucker decomposition AE Cao et al [517] Data parallelization by RenderScript RNN Chen et al [518] Space exploration for data reusability and kernel redundancy removal CNN Rallapalli et al [519] Memory optimizations CNN Lane et al [520] Runtime layer compression and deep architecture decomposition MLP, CNN Huynh et al [521] Caching, Tucker decomposition and computation offloading CNN Wu et al [522] Parameters quantization CNN Bhattacharya and Lane [523] Sparsification of fully-connected layers and separation of convolutional kernels MLP, CNN Georgiev et al [97] Representation sharing MLP Cho and Brand [524] Convolution operation optimization CNN Guo and Potkonjak [525] Filters and classes pruning CNN Li et al [526] Cloud assistance and incremental learning CNN Zen et al [527] Weight quantization LSTM Falcao et al [528] Parallelization and memory sharing Stacked AE Fang et al [529] Model pruning and recovery scheme CNN Xu et al [530] Reusable region lookup and reusable region propagation scheme CNN…”

Section: Tailoring Deep Learning To Mobile Networkmentioning

confidence: 99%

“…Beyond these works, researchers also successfully adapt deep learning architectures through other designs and sophisticated optimizations, such as parameters quantization [522], [527], sparsification and separation [523], representation and memory sharing [97], [528], convolution operation optimization [524], pruning [525], cloud assistance [526] and compiler optimization [532]. These techniques will be of great significance when embedding deep neural networks into mobile systems.…”

Section: A Tailoring Deep Learning To Mobile Devices and Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,328

838

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The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space.In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

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Section: Tailoring Deep Learning To Mobile Networkmentioning

confidence: 99%

Section: A Tailoring Deep Learning To Mobile Devices and Systemsmentioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,328

838

View full text Add to dashboard Cite

show abstract

“…We call such a node, the edge server. Instances of such an architecture recently emerged [6]- [8] to support a variety of real-time applications [9]- [11]. The recent NVIDIA AGX platform line-up is one example of today's GPU-enabled nodes designed to be the edge server in such an architecture (NVIDIA AGX is specifically marketed as the "brain" node supporting autonomous driving [12]).…”

Section: Introductionmentioning

confidence: 99%

Scheduling Real-time Deep Learning Services as Imprecise Computations

Yao

Hao

Zhao³

et al. 2020

2020 IEEE 26th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)

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The paper presents an efficient real-time scheduling algorithm for intelligent real-time edge services, defined as those that perform machine intelligence tasks, such as voice recognition, LIDAR processing, or machine vision, on behalf of local embedded devices that are themselves unable to support extensive computations. The work contributes to a recent direction in real-time computing that develops scheduling algorithms for machine intelligence tasks with anytime predicition. We show that deep neural network workflows can be cast as imprecise computations, each with a mandatory part and (several) optional parts whose execution utility depends on input data. The goal of the real-time scheduler is to maximize average accuracy of deep neural network outputs, while meeting task deadlines, thanks to opportunistic shedding of the least necessary optional parts. The work is motivated by the proliferation of increasingly ubiquitous but resource-constrained embedded devices (for applications ranging from autonomous cars to the Internet of Things) and the desire to develop services that endow them with intelligence. Experiments on recent GPU hardware and a state of the art deep neural network for machine vision illustrate that our scheme can increase the overall accuracy by 10% ∼ 20%, while incurring (nearly) no deadline misses.

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Real-time Context-aware learning System for IoT Applications

Das¹,

Almhana²

2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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FitCNN: A cloud-assisted lightweight convolutional neural network framework for mobile devices

Cited by 9 publications

References 15 publications

Deep Learning in Mobile and Wireless Networking: A Survey

Deep Learning in Mobile and Wireless Networking: A Survey

Scheduling Real-time Deep Learning Services as Imprecise Computations

Real-time Context-aware learning System for IoT Applications

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