A hybrid analytical-ML framework predicts LLM inference latency and energy from architectural parameters, with MAPE below 5 percent on selected models and about 10 percent on a broad set.
Integration of oscillator-based feature extraction for energy- efficient convolutional neural networks.Journal of Applied Physics, 139(23), 2026
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Multi-Level Modeling of Large Language Model Inference Latency and Energy via Hybrid Analytical--Machine-Learning Predictors
A hybrid analytical-ML framework predicts LLM inference latency and energy from architectural parameters, with MAPE below 5 percent on selected models and about 10 percent on a broad set.