The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment (2023.emnlp-main)
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| Challenge: | Inference is estimated to make up 80 to 90% of ML cloud computing demand . |
| Approach: | They propose to identify bottlenecks in deep learning frameworks that are causing the disparity in model latency as hardware speed increases over time. |
| Outcome: | The proposed models show that the framework tax is increasing as the hardware speed increases over time. |
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