Proposes EpG and OOI metrics showing agentic workflows use 4.33x more energy per successful goal than linear baselines due to orchestration structure.
Mlperf power: Benchmarking the energy efficiency of machine learning systems from microwatts to megawatts for sustainable ai
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EnergyLens predicts multi-GPU LLM inference energy consumption with 9-13% MAPE and identifies configurations with up to 52x energy efficiency differences.
Edge AI systems require ongoing adaptation to evolving data and constraints to avoid violating budgets or losing reliability, formalized via an Agent-System-Environment lens that defines ten future research challenges.
citing papers explorer
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Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems
Proposes EpG and OOI metrics showing agentic workflows use 4.33x more energy per successful goal than linear baselines due to orchestration structure.
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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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Position Paper: From Edge AI to Adaptive Edge AI
Edge AI systems require ongoing adaptation to evolving data and constraints to avoid violating budgets or losing reliability, formalized via an Agent-System-Environment lens that defines ten future research challenges.