AI RESEARCH
AutoMCU: Feasibility-First MCU Neural Network Customization via LLM-based Multi-Agent Systems
arXiv CS.LG
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ArXi:2605.21560v1 Announce Type: new Deploying neural networks on microcontroller units (MCUs) is critical for edge intelligence but remains challenging due to tight memory, storage, and computation constraints. Existing approaches, such as model compression and hardware-aware neural architecture search (HW-NAS), often depend on proxy metrics, incur high search cost, and do not fully bridge the gap between architecture design and verified deployment.