Summary of Split Learning in Computer Vision For Semantic Segmentation Delay Minimization, by Nikos G. Evgenidis et al.
Split Learning in Computer Vision for Semantic Segmentation Delay Minimization
by Nikos G. Evgenidis, Nikos A. Mitsiou, Sotiris A. Tegos, Panagiotis D. Diamantoulakis, George K. Karagiannidis
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Information Theory (cs.IT); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to minimize inference delay in semantic segmentation using split learning (SL), tailored to real-time computer vision (CV) applications on resource-constrained devices. The authors highlight the latency challenges in traditional centralized processing methods for CV applications like autonomous vehicles and smart city infrastructure, which often result in unacceptable inference delays. SL is introduced as a promising alternative by partitioning deep neural networks (DNNs) between edge devices and a central server, enabling localized data processing and reducing transmission requirements. The paper’s contributions include joint optimization of bandwidth allocation, cut layer selection for edge devices’ DNN, and central server’s processing resource allocation. Numerical results demonstrate the effectiveness of this approach in reducing inference delay, showcasing its potential for improving real-time CV applications in dynamic environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem with computer vision (CV) on small devices like smartphones or cameras. The issue is that it takes too long to process and analyze what’s happening in videos or images. To fix this, the authors suggest using something called “split learning” (SL). SL breaks down the big neural network into smaller parts that can be processed separately by the device and a central server. This makes processing faster and more efficient. The paper shows how to make this work for different scenarios and proposes simple solutions that still work well but use less energy and computing power. |
Keywords
» Artificial intelligence » Inference » Neural network » Optimization » Semantic segmentation
