  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.
