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Technological developments in artificial intelligence and machine learning have recently been integrated into a range of daily objects to improve our lives. However, this progress has increased memory and energy consumption, causing harm to the environment. Addressing this challenge is critical for the well-being of current and future generations and for ensuring sustainability. The reduction of carbon footprint for neural networks is an essential step toward sustainable AI development. We present a structured examination of neural network optimization which covers the entire pipeline from data preprocessing to model design, compression, and hardware efficiency. Unlike prior works that focus on isolated stages, this survey integrates diverse strategies such as data labeling, feature selection, quantization, pruning, knowledge distillation, and approximate adders into a unified framework. This integration provides a cross-stage perspective that reveals synergies and trade-offs often hidden in fragmented studies, while also offering a consolidated reference for researchers through analysis and benchmarking. The novelty lies in combining a literature-wide synthesis with an interactive benchmarking platform that enables side-by-side comparison of optimization methods across metrics and deployment scenarios. A key contribution is the development of a platform compiling results from 139 peer-reviewed studies (81% from 2020 onward), enabling interactive exploration of accuracy, latency, and energy trade-offs. Validation comes from aggregated cross-study analysis, grounding insights in a broad and current evidence base rather than single experiments. This perspective is particularly valuable for guiding sustainable AI development by identifying trade-offs and synergies across optimization stages.
Ghoneim et al. (Wed,) studied this question.