Neural architecture search for optimizing edge computing in IoT devices
TL;DR
This paper proposes a multi-objective NAS framework that incorporates hardware-aware constraints specific to typical IoT edge platforms, and demonstrates that NAS-generated models outperform conventional architectures in terms of inference speed and power consumption, while maintaining competitive accuracy.
Abstract
The proliferation of Internet of Things (IoT) devices has intensified the demand for efficient and accurate deep learning models capable of operating under stringent resource constraints at the edge. Neural Architecture Search (NAS) offers a promising avenue to automate the design of optimized neural networks tailored for edge computing environments. This paper investigates the application of NAS for optimizing neural network architectures deployed on IoT edge devices, balancing accuracy, latency, and energy efficiency. We propose a multi-objective NAS framework that incorporates hardware-aware constraints specific to typical IoT edge platforms. Experimental results on benchmark datasets demonstrate that NAS-generated models outperform conventional architectures in terms of inference speed and power consumption, while maintaining competitive accuracy. Our findings highlight the potential of NAS as a vital tool for enhancing edge intelligence in IoT systems.