What emerging techniques show the most promise for reducing the size of AI models while preserving their performance metrics? How do these approaches compare across various quantization methods, knowledge distillation frameworks, and pruning strategies?
The HPE-NVIDIA partnership will boost AI to new horizons. It's difficult for me to predict all the application areas, but integrating them into my stochastic model will make it more efficient.
https://www.academia.edu/43796598/Stochastic_Model_and_Worldwide_Conflict_Resolution_chaos_control_modeling_and_simulation
Emerging techniques like quantization, knowledge distillation, and pruning reduce AI model size while maintaining performance. Quantization lowers precision to save memory, distillation trains smaller models to mimic larger ones, and pruning removes redundant components. Advanced methods, such as QAT, multi-teacher distillation, and structured pruning, enhance efficiency with minimal accuracy loss. Combining these approaches, like pruning with quantization, optimizes lightweight, high-performing models for real-world deployment.
I have a silly question that is somewhat related: should we be calling it quantization or discretization? Or re-sampling? Or something else? Is quantization the correct word?
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