The process of updating deep learning/AI models when they face new tasks or must accommodate changes in data can have significant costs in terms of computational resources and energy consumption.
Researchers have developed a novel method that predicts those costs, allowing users to make informed decisions about when to update AI models to improve AI sustainability.
"If we want to address sustainability issues related to deep learning AI, we must look at computational and energy costs across a model's entire life cycle -- including the costs associated with updates. If you cannot predict what the costs will be ahead of time, it is impossible to engage in the type of planning that makes sustainability efforts possible. That makes our work here particularly valuable.
"Regardless of what is driving the need for an update, it is extremely useful for AI practitioners to have a realistic estimate of the computational demand that will be required for the update," Kim says."This can help them make informed decisions about when to conduct the update, as well as how much computational demand they will need to budget for the update."
Those costs are presented as a single index value, which can then be compared with five metrics: epochs, parameter change, gradient norm, carbon and energy. Epochs, parameter change and gradient norm are all ways of measuring the amount of computational effort necessary to retrain the model. "We found that the RESQUE predictions aligned very closely with the real-world costs of conducting deep learning model updates," Kim says."Also, as I noted earlier, all of our experimental findings tell us that training a new model from scratch demands far more computational power and energy than retraining an existing model."
"In the bigger picture, this work offers a deeper understanding of the costs associated with deep learning models across their entire life cycle, which can help us make informed decisions related to the sustainability of the models and how they are used. Because if we want AI to be viable and useful, these models must be not only dynamic but sustainable."Researchers have developed a new framework to make complex AI tasks more accessible and transparent to users.
Energy Technology Energy Policy Petroleum Computer Modeling Mathematical Modeling Computational Biology Mathematics
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