Summary of Latenrgy: Model Agnostic Latency and Energy Consumption Prediction For Binary Classifiers, by Jason M. Pittman
Latenrgy: Model Agnostic Latency and Energy Consumption Prediction for Binary Classifiers
by Jason M. Pittman
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 tackles challenges in compute overhead during inference in machine learning (ML) systems, which hinders their scalability and sustainability. The authors identify gaps in the literature regarding predictive techniques for latency and energy consumption, cross-comparisons of classifiers, and the impact of responsible AI (RAI) guardrails on inference performance. A model-agnostic framework is proposed to predict latency and energy consumption using Theory Construction Methodology. Two predictive equations are derived that capture the interplay between classifier characteristics, dataset properties, and RAI guardrails. This work provides foundational insights for designing efficient and responsible ML systems, enabling researchers to optimize inference performance and practitioners to deploy scalable solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps make machine learning more sustainable by finding ways to make it faster and use less energy. It fills in some missing pieces in the field of AI research. The authors created a new way to predict how long it takes for an AI model to do its job and how much energy it uses. They did this by looking at different things that affect the performance of the model, like what kind of data it’s trained on and what rules are in place to make sure the AI is fair and safe. This new framework can help people design better AI models that use less power and time. It also helps make sure that AI systems are working responsibly. |
Keywords
» Artificial intelligence » Inference » Machine learning
