  Hardware-aware Neural Architecture Search (NAS) technologies have been
proposed to automate and speed up model design to meet both quality and
inference efficiency requirements on a given hardware. Prior arts have shown
the capability of NAS on hardware specific network design. In this whitepaper,
we further extend the use of NAS to Intel Movidius VPU (Vision Processor
Units). To determine the hardware-cost to be incorporated into the NAS process,
we introduced two methods: pre-collected hardware-cost on device and
device-specific hardware-cost model VPUNN. With the help of NAS, for
classification task on VPU, we can achieve 1.3x fps acceleration over
Mobilenet-v2-1.4 and 2.2x acceleration over Resnet50 with the same accuracy
score. For super resolution task on VPU, we can achieve 1.08x PSNR and 6x
higher fps compared with EDSR3.
