Summary of Fusedinf: Efficient Swapping Of Dnn Models For On-demand Serverless Inference Services on the Edge, by Sifat Ut Taki et al.
FusedInf: Efficient Swapping of DNN Models for On-Demand Serverless Inference Services on the Edge
by Sifat Ut Taki, Arthi Padmanabhan, Spyridon Mastorakis
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The Edge AI computing boxes aim to revolutionize the AI industry by processing AI directly on the edge of the network. On-demand serverless inference services are gaining popularity, but they require efficient model loading and unloading. To address this challenge, we introduce FusedInf, a method that combines multiple DNN models into a single Direct Acyclic Graph (DAG) to improve GPU memory utilization and execution speed. Our evaluation shows that FusedInf can reduce memory requirements by up to 17% and accelerate model execution by up to 14%. The prototype implementation is available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FusedInf is a new way to make AI work faster and more efficiently. Right now, when you want to use AI on the edge of the network, it can take a long time to load all the needed information into your device. FusedInf solves this problem by combining many different AI models into one single package that takes up less space and works faster. This means that businesses with small or medium-sized data needs can get started with AI more quickly and at a lower cost. |
Keywords
* Artificial intelligence * Inference
