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ESCADE: Energy-efficient Artificial Intelligence for Cost-effective and Sustainable Data Centers

Sabine Janzen; Hannah Stein; Katharina Trinley; Cicy Agnes; Vaibhav Jain; Karan Rajeshkar; Nirav Shenoy; Anika Rusch; Sujatro Ghosh; Wolfgang Maaß
In: RPEatCAiSE25: Research Projects Exhibition at the International Conference on Advanced Information Systems Engineering, June 16-20, 2025, Vienna, Austria. International Conference on Advanced Information Systems Engineering (CAiSE-2025), 6/2025.

Abstract

Data centers play a central role in digital transformation, especially in the field of artificial intelligence (AI). However, their energy consumption is enormous, e.g., 16 billion kWh in Germany in 2020. At the same time, energy costs are rising and climate neutrality requirements are increasing. These factors pose major challenges for the sustainable and cost-effective operation of data centers. This paper introduces the ESCADE project (05/2023 - 04/2026), an ongoing research initiative funded by the German Federal Ministry of Economics and Climate Action, aiming to optimize the energy-efficiency of AI in data centers. AI compression techniques such as knowledge distillation, quantization and neural architecture search result in smaller, more energy-efficient AI models that deliver comparable performance. When combined with neuromorphic hardware, these models can achieve energy savings of up to 80The ESCADE consortium, a multidisciplinary collaboration of seven industry and academic partners, explores energy- efficient AI in two use cases: visual computing for scrap sorting in steel industry and natural language processing for software development. This paper provides a comprehensive overview of the ESCADE project, outlining its objectives, work packages, and anticipated outcomes. A central contribution is the introduction of first results in terms of the information system EAVE: Energy Analytics for Cost-effective and Sustainable Operations. By using AI-based analyses, EAVE optimizes the relationship between AI performance and operating costs of AI applications in data centers. The system measures and predicts the energy consumption, CO emissions and operating costs of different AI model configurations, including hardware options. At the same time, it analyzes which factors significantly influence these values. This enables decision-makers to manage the operation of data centers in a data-based and efficient manner while meeting environmental targets.

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