Neural architecture search automates the design of neural network architectures usually by exploring a large and thus complex architecture search space. To advance the architecture search, we present a graph diffusion-based NAS approach that uses discrete conditional graph diffusion processes to generate high-performing neural network architectures. We then propose a multi-conditioned classifier-free guidance approach applied to graph diffusion networks to jointly impose constraints such as high accuracy and low hardware latency. Unlike the related work, our method is completely differentiable and requires only a single model training. In our evaluations, we show promising results on six standard benchmarks, yielding novel and unique architectures at a fast speed, i.e. less than 0.2 seconds per architecture. Furthermore, we demonstrate the generalisability and efficiency of our method through experiments on ImageNet dataset.

翻译：神经架构搜索通常通过探索庞大且复杂的架构搜索空间来自动化设计神经网络架构。为推进架构搜索，我们提出了一种基于图扩散的NAS方法，该方法利用离散条件图扩散过程生成高性能神经网络架构。我们进一步提出一种应用于图扩散网络的多条件无分类器引导方法，以联合施加高精度与低硬件延迟等约束。与相关工作不同，我们的方法完全可微且仅需单次模型训练。在六项标准基准测试中，我们展示了该方法在快速生成新颖且独特架构方面的优异表现（每架构生成时间小于0.2秒）。此外，通过ImageNet数据集上的实验，我们证明了该方法的泛化性与高效性。