Summary of Fluidml: Fast and Memory Efficient Inference Optimization, by Jinjie Liu et al.
FluidML: Fast and Memory Efficient Inference Optimization
by Jinjie Liu, Hang Qiu
First submitted to arxiv on: 14 Nov 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 Machine learning models deployed on edge devices have enabled numerous exciting new applications, such as humanoid robots, AR glasses, and autonomous vehicles. The ever-growing number of parameters in these models poses a challenge for computing resources available on these edge devices. To address this issue, we present FluidML, a generic runtime memory management and optimization framework that transforms the model execution blueprint to achieve faster and more memory-efficient inference. This framework consistently reduces end-to-end inference latency by up to 25.38% for popular language models and peak memory usage by up to 41.47%, outperforming state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is being used in many cool new devices, like robots and augmented reality glasses. The problem is that these devices don’t have enough power to handle all the calculations needed. To fix this, we created a special program called FluidML that helps make the calculations faster and uses less memory. We tested it on some popular language models and found that it can speed up processing by up to 25% and reduce memory usage by up to 41%. This new framework is being released as open-source so other researchers can use it too. |
Keywords
* Artificial intelligence * Inference * Machine learning * Optimization
