Cloud-Based or On-Device: An Empirical Study of Mobile Deep Inference

By Tian Guo. 2017

71 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Young Geun Kim
2. Carole-Jean Wu
3. Zsolt Kira
4. Hongxia Jin
5. Marco Levorato

In Electrical and Electronic Engineering

1. Young Geun Kim
2. Carole-Jean Wu
3. Zsolt Kira
4. Francesco Restuccia
5. Marco Levorato
6. Yen-Chang Hsu
7. Yoshitomo Matsubara

Citations in the corpus

• Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning (2021, background): “This is concerning for many computer vision applications because 1) Many computer vision applications are on-device and therefore memory constrained [17, 37, 54], and 2) Many computer vision applications learn from data which cannot be legally stored [6, 12, 64].”
• AutoScale: Energy Efficiency Optimization for Stochastic Edge Inference Using Reinforcement Learning (2020, methodology): “As an intermediate stage, many techniques tried to partition DNN inference execution between the cloud and the local mobile device [24], [25], [40], [53], [69], using performance/energy prediction models.”
• Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges (2021, background): “Without such natural bottlenecks in the model, straightforward splitting approaches would fail to improve performance in most settings [6, 35].”
• AutoScale: Optimizing Energy Efficiency of End-to-End Edge Inference under Stochastic Variance (2020, methodology): “As an intermediate stage, many techniques tried to partition DNN inference execution between the cloud and local mobile device [21, 22, 34, 44, 59], based on performance/energy prediction models.”