Green AI techniques for reducing energy consumption in AI systems

This systematic review synthesizes current evidence on energy-reduction techniques across algorithmic, hardware, and infrastructure layers of AI systems. Model compression and knowledge distillation (e.g., DistilBERT) deliver ∼60% faster inference with ∼40% fewer parameters while retaining ∼97% of baseline performance. Low-precision computation (quantization) yields up to ∼50% energy reductions, and architecture-level strategies—such as neural architecture search and depthwise-separable convolutions in MobileNetV2—significantly lower compute and memory demand. Specialized accelerators (TPUs) and neuromorphic hardware further improve efficiency, while data-center measures (advanced cooling, virtualization, renewable integration) reduce system-level consumption. For generative-AI workloads, distillation, quantization, efficient architectures, and accelerator-optimized inference remain the primary pathways to lowering both training and inference energy. Across studies, recurring gaps include inconsistent energy-metric reporting, limited standardized benchmarks, and a dominant focus on accuracy over efficiency. Regulatory progress is uneven: the EU has introduced stronger transparency requirements, whereas comparable obligations are not yet global. Review limitations include heterogeneous methodologies and incomplete transparency artifacts, which restrict cross-study comparability. Future research directions include algorithm–hardware co-design, neuromorphic methods, energy-harvesting AI devices, improved data-center operations, and explainable-AI tools to support reliable, energy-aware deployment at scale.