DEff-ARTS: differentiable efficient ARchiTecture search

Manual design of efficient Deep Neural Networks (DNNs) for mobile and edge devices is an involved process which requires expert human knowledge to improve efficiency in different dimensions. In this paper, we present DEff-ARTS, a differentiable efficient architecture search method for automatically deriving CNN architectures for resource constrained devices. We frame the search as a multi-objective optimisation problem where we minimise the classification loss and the computational complexity of performing inference on the target hardware. Our formulation allows for easy trading-off between the sub-objectives depending on user requirements. Experimental results on CIFAR-10 classification showed that our approach achieved a highly competitive test error rate of 3:24% with 30% fewer parameters and multiply and accumulate (MAC) operations compared to Differentiable ARchiTecture Search (DARTS)