How can we make machine learning models ... - tlooto, The Most Powerful AcademicGPT

Enhancing the energy efficiency of machine learning (ML) models is both an urgent engineering challenge and a developing area of research, given the proliferation of large-scale models and their mounting computational and environmental costs. Achieving meaningful improvements in energy efficiency requires a comprehensive, multi-layered approach integrating algorithm-level innovations, hardware-software codesign, operational strategies, and data-aware practices. 1. Algorithmic and Model-Level Optimizations Model pruning and quantization are foundational strategies: pruning reduces redundant parameters, thereby lowering theoretical FLOPs and memory usage, while quantization drops precision (e.g., from float32 to int8), which can yield faster computations and decreased energy requirements, especially when deployed on specialized hardware[1]. Knowledge distillation—training compact student models with large teacher model outputs—retains performance while cutting inference and training cost