Summary of Distributed Inference on Mobile Edge and Cloud: An Early Exit Based Clustering Approach, by Divya Jyoti Bajpai and Manjesh Kumar Hanawal
Distributed Inference on Mobile Edge and Cloud: An Early Exit based Clustering Approach
by Divya Jyoti Bajpai, Manjesh Kumar Hanawal
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 Recent advancements in Deep Neural Networks (DNNs) have led to exceptional performance across various domains, but their large size poses a challenge for deployment on resource-constrained devices like mobile, edge, and IoT platforms. To address this, researchers proposed a distributed inference setup where a small-sized DNN is deployed on mobile, a bigger version on the edge, and the full-fledged one on the cloud. This approach raises the question of how to determine complexity so that it’s processed by enough layers of DNNs. The paper develops a novel method called DIMEE, which utilizes Early Exit (EE) strategies to minimize inference latency in DNNs while improving accuracy and considering offloading costs from mobile to edge/cloud. Experimental validation on GLUE datasets, encompassing various NLP tasks, shows that our approach significantly reduces the inference cost (> 43%) while maintaining a minimal drop in accuracy (< 0.3%) compared to the case where all inference is made in the cloud. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super powerful computer, but it’s too big and heavy for your pocket or purse. That’s kind of like what happens with Deep Neural Networks (DNNs) – they’re really good at doing certain tasks, but they take up a lot of space and energy. To solve this problem, scientists came up with an idea called distributed inference. It’s like having a small, portable computer that can do some things, while the bigger one does more complicated tasks in the cloud. The question is – how do we decide which task to do where? A new approach called DIMEE tries to answer this by using strategies to make the process faster and more accurate. Tests on special datasets show that it works really well, reducing energy use by a lot (over 43%) while still keeping the accuracy high. |
Keywords
» Artificial intelligence » Inference » Nlp
