Green AI: A Comprehensive Framework for Energy-, Latency-, Memory-, and Carbon-Efficient Deep Learning Model Optimization

Abstract

The rapid growth of deep learning models has increased computational requirements, energy consumption, and carbon emissions, raising concerns about the sustainability of artificial intelligence systems. Existing compression techniques, such as pruning and quantization, are typically evaluated in isolation and provide limited guidance for selecting energy-efficient configurations. To address this challenge, this paper presents a Green AI framework that predicts and recommends model compression strategies based on their expected efficiency gains within an empirically validated candidate space. The framework profiles pruning and quantization across nine architectures and employs supervised learning models to estimate their effects on memory usage, inference latency, energy consumption, and CO2 emissions. A recommendation module then ranks candidate configurations according to a user-specified optimization objective. The framework was evaluated using 276 profiling experiments spanning seven convolutional neural networks (ResNet18, ResNet50, VGG11, VGG16, MobileNet-V2, MobileNet-V3-Small, and EfficientNet-B0) and two Transformer models (BERT and DistilBERT) in a CPU-only deployment environment. Memory and latency prediction achieved R2 values above 0.95, while energy and CO2 reduction prediction achieved mean absolute errors of 3.27 and 2.85 percentage points, respectively, with R2 values of 0.71 and 0.73. Quantization generally produced larger energy and CO2 reductions than pruning, whereas pruning provided more moderate efficiency gains. However, six of the nine architectures exhibited increased runtime memory usage after compression, demonstrating that memory must be treated as an independent deployment constraint. Task-level evaluation further showed that INT8 quantization reduced BERT classification accuracy by only 1.3 percentage points. Results indicate that the optimal compression strategy is strongly architecture-dependent, highlighting the value of data-driven recommendation over fixed heuristic selection. Validation is performed against the closest experimentally observed configurations; extending the framework to previously unseen architectures remains an important direction for future work.