Brief

Researchers have developed AutoMCU, a novel system that leverages LLM-based multi-agent approaches to customize neural networks for microcontroller units (MCUs). This method prioritizes feasibility by integrating vendor toolchain feedback early in the design process, significantly reducing the search cost and time compared to traditional hardware-aware neural architecture search methods. AutoMCU has demonstrated competitive accuracy on benchmark datasets and successful deployment on STM32 microcontrollers, making edge intelligence more accessible. AI

Researchers have developed a novel method called Code-Oriented LM Embeddings (COLE) to improve Neural Architecture Search (NAS). This technique uses off-the-shelf language models to generate embeddings from code representations of neural architectures, bypassing the need for expensive fine-tuning or complex feature engineering. Experiments on NAS-Bench-201 and einspace demonstrated that COLE embeddings outperform other text-based encodings and significantly reduce the evaluation budget required to find high-performing architectures. AI