Summary of Agreement-based Cascading For Efficient Inference, by Steven Kolawole et al.
Agreement-Based Cascading for Efficient Inference
by Steven Kolawole, Don Dennis, Ameet Talwalkar, Virginia Smith
First submitted to arxiv on: 2 Jul 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 This paper presents an adaptive inference technique called Agreement-Based Cascading (ABC), which reduces the cost of machine learning inference by assigning smaller models to easier examples and using agreement between ensembles of models at each level as a basis for data-dependent routing. ABC builds a cascade of models of increasing size/complexity, and while ensemble execution introduces additional expense, it can be easily offset due to large expected differences in model sizes, parallel inference execution capabilities, and accuracy benefits of ensembling. The paper theoretically and empirically examines ABC’s performance relative to existing cascading methods, showing that it can reliably act as a drop-in replacement for existing models and surpass the best single model it aims to replace in terms of both efficiency and accuracy. ABC achieves significant reductions in costs in three common scenarios: edge-to-cloud inference (up to 14x), cloud-based model serving (3x), and inference via model API services (2-25x relative to state-of-the-art LLM cascades). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make machine learning faster and cheaper. It’s called Agreement-Based Cascading, or ABC for short. Right now, when we do machine learning, we use big models that can be slow and expensive. But what if we could use smaller models for easier problems, and only bring out the bigger models when needed? That’s basically what ABC does. It builds a team of small and big models, and uses how well they agree with each other to decide which model to use for each problem. This makes machine learning faster and cheaper in three different ways: it saves time and money by not using the biggest models all the time, it reduces costs by using smaller models that are less expensive, and it helps us do more complex tasks by bringing in the big guns only when needed. |
Keywords
* Artificial intelligence * Inference * Machine learning
