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

Toward Edge-Efficient Dense Predictions with Synergistic Multi-Task Neural Architecture Search