arXiv:2606.27841v1 Announce Type: new Abstract: The widespread adoption of Artificial Intelligence (AI) has led to increasing concerns about energy consumption, yet there is a lack of standardized methodologies to accurately estimate AI inference energy consumption, particularly across various tasks and architectures. In this study, we propose a task independent, layer-wise energy estimation model for AI architectures. Our model is evaluated on a large dataset of more than 100,000 layers for 295 neural network architectures across 3 widely-used tasks and 3 distinct hardware platforms. Our appr
Amidst the widespread adoption of AI, there's a growing awareness and concern regarding its energy consumption, making accurate estimation methodologies critical for sustainable development.
Accurate AI inference energy estimation is crucial for optimizing hardware, managing data center power demands, and addressing environmental concerns related to AI's expanding footprint.
The proposed WattLayer model offers a standardized, task-independent framework for estimating AI energy use, providing a new tool for developers and policymakers to manage and mitigate consumption.
- · AI hardware manufacturers
- · Data center operators
- · AI model developers
- · Energy efficiency consulting
- · Inefficient AI hardware designs
- · Organizations with high, unoptimized AI inference costs
More energy-efficient AI models and hardware will be developed and adopted.
This could lead to new regulatory frameworks or industry standards around AI energy consumption.
Improved energy efficiency might enable the deployment of AI in more constrained environments, potentially accelerating AI adoption globally.
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Read at arXiv cs.LG