https://dasya.itu.dk/tags/energy-usage/ Recent content in energy usage on DASYA Hugo -- gohugo.io en-us © DASYA, IT University of Copenhagen Tue, 30 Sep 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-nas/ Tue, 30 Sep 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-nas/ Designing neural networks that are both accurate and efficient has become an urgent challenge as the cost of deploying machine learning systems continues to grow. Traditional Neural Architecture Search (NAS) methods usually optimize for accuracy under constraints such as FLOPs, model size, or latency. However, in practice the lifecycle cost of a model is not only determined by training, but also by inference. While training is a one-off expense, inference is repeated continuously once a model is deployed, and depending on the number of queries served, it can become the dominant contributor to energy use and cost.