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Combining Cloud and Mobile Computing for Machine Learning

arXiv 2402.04880 ↗First posted 20 Jan 2024cs.DC cs.AI cs.LGcited by 0

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Abstract (arXiv)

Although the computing power of mobile devices is increasing, machine learning models are also growing in size. This trend creates problems for mobile devices due to limitations like their memory capacity and battery life. While many services, like ChatGPT and Midjourney, run all the inferences in the cloud, we believe a flexible and fine-grained task distribution is more desirable. In this work, we consider model segmentation as a solution to improving the user experience, dividing the computation between mobile devices and the cloud in a way that offloads the compute-heavy portion of the model while minimizing the data transfer required. We show that the division not only reduces the wait time for users but can also be fine-tuned to optimize the workloads of the cloud. To achieve that, we design a scheduler that collects information about network quality, client device capability, and job requirements, making decisions to achieve consistent performance across a range of devices while reducing the work the cloud needs to perform.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 41 references that are in the map. First posted on arXiv 20 Jan 2024.

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20 of 41 references.

References · 20

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8 more
  1. 2020Improving Image Captioning with Better Use of Captionscited by 84
  2. 2020Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologiescited by 41
  3. 2022Machine Learning Model Sizes and the Parameter Gapcited by 39
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  7. 2014The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Veloxcited by 8
  8. 2020Cloud-gaming:Analysis of Google Stadia trafficcited by 4

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