Summary of Efficient Post-training Augmentation For Adaptive Inference in Heterogeneous and Distributed Iot Environments, by Max Sponner and Lorenzo Servadei and Bernd Waschneck and Robert Wille and Akash Kumar
Efficient Post-Training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments
by Max Sponner, Lorenzo Servadei, Bernd Waschneck, Robert Wille, Akash Kumar
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 an automated augmentation flow for creating Early Exit Neural Networks (EENNs), which enhance the efficiency of neural network deployments on heterogeneous or distributed hardware targets. The authors claim that their framework is the first to automate all necessary design decisions, including architecture construction, subgraph mapping, and decision mechanism configuration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create special kinds of neural networks called Early Exit Neural Networks (EENNs) that make computers work faster. It’s hard to make these EENNs because it requires a lot of knowledge about how to design them. To solve this problem, the researchers created a tool that can turn an existing neural network into an EENN by itself. This means the tool decides what the architecture should be, where different parts of the network go on different computer chips, and how the network makes decisions. |
Keywords
* Artificial intelligence * Neural network
