  In this work, we propose a novel and scalable solution to address the
challenges of developing efficient dense predictions on edge platforms. Our
first key insight is that MultiTask Learning (MTL) and hardware-aware Neural
Architecture Search (NAS) can work in synergy to greatly benefit on-device
Dense Predictions (DP). Empirical results reveal that the joint learning of the
two paradigms is surprisingly effective at improving DP accuracy, achieving
superior performance over both the transfer learning of single-task NAS and
prior state-of-the-art approaches in MTL, all with just 1/10th of the
computation. To the best of our knowledge, our framework, named EDNAS, is the
first to successfully leverage the synergistic relationship of NAS and MTL for
DP. Our second key insight is that the standard depth training for multi-task
DP can cause significant instability and noise to MTL evaluation. Instead, we
propose JAReD, an improved, easy-to-adopt Joint Absolute-Relative Depth loss,
that reduces up to 88% of the undesired noise while simultaneously boosting
accuracy. We conduct extensive evaluations on standard datasets, benchmark
against strong baselines and state-of-the-art approaches, as well as provide an
analysis of the discovered optimal architectures.
