FL-NAS: Towards Fairness of NAS for Resource Constrained Devices via
  Large Language Models
Ruiyang Qin Yuting Hu Zheyu Yan Jinjun Xiong Ahmed Abbasi Yiyu Shi
Neural Architecture Search (NAS) has become the de fecto tools in the
industry in automating the design of deep neural networks for various
applications, especially those driven by mobile and edge devices with limited
computing resources. The emerging large language models (LLMs), due to their
prowess, have also been incorporated into NAS recently and show some promising
results. This paper conducts further exploration in this direction by
considering three important design metrics simultaneously, i.e., model
accuracy, fairness, and hardware deployment efficiency. We propose a novel
LLM-based NAS framework, FL-NAS, in this paper, and show experimentally that
FL-NAS can indeed find high-performing DNNs, beating state-of-the-art DNN
models by orders-of-magnitude across almost all design considerations.