AutoMCU: Feasibility-First MCU Neural Network Customization via LLM-based Multi-Agent Systems
arXiv:2605.21560v1 Announce Type: new Abstract: 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. This paper presents AutoMCU, a feasibility-first large language model (LLM)-based multi-agent system for automated neural network cust
The increasing demand for edge AI inference and the limitations of current deployment methods are driving innovation in efficient neural network customization for MCUs.
Efficient AI deployment on microcontrollers is crucial for scaling edge intelligence across numerous applications, reducing latency, and improving data privacy.
The ability to more effectively customize and deploy neural networks on resource-constrained devices makes edge AI more feasible and widespread.
- · Edge AI providers
- · Microcontroller manufacturers
- · IoT device developers
- · LLM developers
- · Inefficient edge AI deployment methods
- · Cloud-dependent AI applications
Increased performance and reduced energy consumption for AI at the edge.
Expansion of AI capabilities into new domains where resource constraints were previously prohibitive.
Enhanced autonomy and decision-making for embedded systems and IoT devices without constant cloud connectivity.
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Read at arXiv cs.LG