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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 presents BearingNAS, a hardware-aware neural architecture search framework that designs intelligent fault diagnosis systems for bearings, targeting microcontrollers and sensor processing units with extremely limited memory (4-8 KiB RAM, 16-32 KiB Flash) while running entirely on a laptop CPU and achieving 99.50% diagnostic accuracy.