AI models have the potential for huge carbon footprints. To make them energy efficient, we need to tune innovations in data usage, hardware and software, argues Imec’s Axel Nackaerts.
Artificial intelligence and machine learning algorithms are becoming mainstream in multiple domains, from industrial diagnostics, healthcare, city management to personal voice assistants and robot vacuum cleaners. As their user base grows, so does the total energy consumption from the creation, training and execution of these algorithms. In particular, the recent introduction of gigantic models, consisting of hundreds of billions of parameters, raised questions on their environmental impact.
To get a better understanding of the total footprint of AI models, we should consider two factors: training and inference. First, an AI model needs to be trained by a labeled dataset. The ever-growing trend towards the use of bigger datasets for this training phase causes an explosive growth in energy consumption. Researchers from the University of Massachusetts calculated that during the training of a model for natural language processing, 284 metric tons of carbon dioxide is emitted. This is equivalent to the emission of five cars during their entire lifespan, including construction. Some AI models developed by tech giants – not reported on in scientific literature – might be even orders of magnitude bigger.
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