<ul><li>MicroNAS is an automated neural architecture search tool designed for developing models optimized for microcontrollers with small memory resources.</li><li>It uses a novel method that considers the memory size of the target microcontroller to optimize convolutional neural network and gated recurrent unit architectures.</li><li>A comparison is made between memory-driven model optimization and traditional pruning methods, demonstrating the effectiveness of MicroNAS.</li><li>MicroNAS achieved higher F1-scores in developing a fall detection system (FDS), showing its potential in real-time FDS development for microcontroller platforms with limited memory.</li></ul>

MicroNAS: An Automated Framework for Developing a Fall Detection System

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