  Hardware-aware neural architecture designs have been predominantly focusing
on optimizing model performance on single hardware and model development
complexity, where another important factor, model deployment complexity, has
been largely ignored. In this paper, we argue that, for applications that may
be deployed on multiple hardware, having different single-hardware models
across the deployed hardware makes it hard to guarantee consistent outputs
across hardware and duplicates engineering work for debugging and fixing. To
minimize such deployment cost, we propose an alternative solution,
multi-hardware models, where a single architecture is developed for multiple
hardware. With thoughtful search space design and incorporating the proposed
multi-hardware metrics in neural architecture search, we discover
multi-hardware models that give state-of-the-art (SoTA) performance across
multiple hardware in both average and worse case scenarios. For performance on
individual hardware, the single multi-hardware model yields similar or better
results than SoTA performance on accelerators like GPU, DSP and EdgeTPU which
was achieved by different models, while having similar performance with
MobilenetV3 Large Minimalistic model on mobile CPU.
