LLM-guided framework optimizes neural networks for physical hardware

By PulseAugur Editorial · [2 sources] · 2026-06-09 01:32

Researchers have developed a new framework called UH-NAS, which uses LLMs to guide neural architecture search for physical neural networks. This approach co-optimizes task accuracy with hardware constraints like energy consumption and physical non-idealities. UH-NAS is designed to be hardware-agnostic, allowing for fair comparisons across different computing platforms and discovering more robust architectures than traditional methods. AI

RANK_REASON The cluster contains a research paper detailing a new method for neural architecture search.

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AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

arXiv cs.AI TIER_1 English(EN) · Tyler King, Timothee Leleu · 2026-06-10 04:00

LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks

arXiv:2606.10294v1 Announce Type: cross Abstract: Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as energy cost, physical non-idealities, and numerical precision. Existing neural arc…
arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Timothee Leleu · 2026-06-09 01:32

LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks

Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as energy cost, physical non-idealities, and numerical precision. Existing neural architecture search (NAS) methods are typically tailo…

COVERAGE [2]

LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks

LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks

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