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NeurIPS 2026SpotlightGeorgia TechConstellationMila & Université de MontréaGeorgia Institute of TechnologyU California, DavisOn-device ML

FETTUCCINE: Fast and efficient brain-to-text decoding on mobile devices

Jonathan McCart, Pranav Deevi, Mehdi Azabou, Nanda H Krishna and 4 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Institute of automation, ChineseTsinghuaLi Auto Inc.University College London, UniveLi AutoOn-device ML

Dancing in Fetters: Pareto-Optimal On-Device LLMs under Hardware Constraints

Luoyang Sun, Jiwen Jiang, Yifeng Ding, Fengfa Li and 8 more

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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NeurIPS 2026StanfordTogether AIGoogle BrainStanfordOn-device ML

Intelligence per Watt: Measuring Intelligence Efficiency of Local AI

Proposing intelligence per watt to evaluate local LLM inference, the study finds local models answer 88.7% of queries with 5.3x efficiency gains since 2023 but remain 1.4x less efficient than cloud accelerators.

Jon Saad-Falcon, Avanika Narayan, Hakki Akengin, J. W Griffin and 10 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 17 on Hugging Face · Code ★ 95

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AI panel: 16 of 20 reviewers recommend it
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