EnergyLens predicts multi-GPU LLM inference energy consumption with 9-13% MAPE and identifies configurations with up to 52x energy efficiency differences.
Reinforcement Learning-Guided Graph State Generation in Photonic Quantum Computers
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Tree-encoded fusion with spin qubit memory suppresses erasure errors in photonic graph-state generation, yielding exponential compiler gains over OneAdapt on benchmarks and hardware validation.
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EnergyLens: Predictive Energy-Aware Exploration for Multi-GPU LLM Inference Optimization
EnergyLens predicts multi-GPU LLM inference energy consumption with 9-13% MAPE and identifies configurations with up to 52x energy efficiency differences.
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Suppressing the Erasure Error of Fusion Operation in Photonic Quantum Computing
Tree-encoded fusion with spin qubit memory suppresses erasure errors in photonic graph-state generation, yielding exponential compiler gains over OneAdapt on benchmarks and hardware validation.