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ENAS is a hardware-aware Neural Architecture Search framework that operates efficiently on CPUs without GPUs, achieving speedups and competitive accuracy for TinyML models on resource-constrained microcontrollers.
The paper introduces TGL-NSGA-II, a teacher-guided fitness approximation framework for expensive evolutionary optimization in constrained TinyML neural architecture search, achieving improved efficiency and reliability over standard methods.
This paper introduces a streamlined pipeline for training and deploying machine learning models on the WeBe Band wearable device, focusing on system-level automation and hardware-aware optimization to enable rapid iteration and on-device evaluation for edge AI applications.
This paper presents ARGO, a smart eyewear platform for on-device machine learning that integrates a multimodal sensor suite and an optimized YOLOv11 model for real-time urban obstacle recognition, achieving privacy-preserving local processing with low latency and a memory footprint of 2.483 MB.
Proposes a lightweight neural architecture search performed directly on the deployment device for near-sensor computing, validated on sEMG sign language and fault diagnosis datasets, achieving improved accuracy and reduced RAM occupancy.
This paper presents an AI-driven framework for energy-efficient environmental monitoring in smart cities using edge intelligence and TinyML, which dynamically activates sensors based on spatiotemporal conditions to reduce energy consumption and extend sensor lifespan.