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Added citations for sustainable ML, energy-efficient computing, mixed precision training, and TinyML benchmarking to strengthen the future work discussion. New citations: - Strubell et al. (2019): Energy and Policy Considerations for Deep Learning in NLP - foundational work on ML carbon footprint - Patterson et al. (2021): Carbon Emissions and Large Neural Network Training - comprehensive analysis of energy use in large models - Micikevicius et al. (2018): Mixed Precision Training - ICLR paper on FP16/FP32 training techniques - Banbury et al. (2021): Benchmarking TinyML Systems - TinyMLPerf benchmarking framework for edge AI Citations integrated into: - Roofline Models section (mixed precision advantages) - Energy and Power Profiling section (sustainable ML and edge AI) These citations ground the future work proposals in established research on green AI, energy-efficient ML, and edge deployment. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>