Implementation and Performance of Artificial Intelligence Systems
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 2
Special Issue Editor
Special Issue Information
Dear Colleagues,
The universal popularity and exploding energy consumption of large-scale artificial intelligence models such as LLMs pose a potentially insoluble problem to our societies. How can we generalize the use of these tools while achieving the energy- and hardware savings required by global environmental and climate-stabilization goals? The proliferation of Large Language Models (LLMs) has marked a paradigm shift in artificial intelligence.
However, their immense computational and memory requirements present a significant barrier to their widespread deployment. The operational costs, energy consumption, and hardware demands associated with running these models at scale are substantial. Consequently, research into model compression and optimization has become a critical field of study.
In this Special Issue, we invite researchers and practitioners to submit their work on the measurement, modeling and optimization of the energy costs for ML/AI training and inference. Predicting computation time is part of predicting the energy cost of a given computation. Work on parallel-distributed performance modeling is thus also welcome if it is applied to general AI or more specifically very large models. We also welcome up-to-date critical literature surveys on this topic of great scientific, technological and ecological importance.
Prof. Dr. Gaetan Hains
Guest Editor
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Keywords
- performance of AI and ML systems
- embedded AI inference
- energy consumption
- green-IT
- measuring, predicting and optimizing
- quantization, algorithm complexity
- static analysis and cost models
- implementation and performance of artificial intelligence systems
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