  Recent breakthroughs in Neural Architectural Search (NAS) have achieved
state-of-the-art performances in applications such as image classification and
language modeling. However, these techniques typically ignore device-related
objectives such as inference time, memory usage, and power consumption.
Optimizing neural architecture for device-related objectives is immensely
crucial for deploying deep networks on portable devices with limited computing
resources. We propose DPP-Net: Device-aware Progressive Search for
Pareto-optimal Neural Architectures, optimizing for both device-related (e.g.,
inference time and memory usage) and device-agnostic (e.g., accuracy and model
size) objectives. DPP-Net employs a compact search space inspired by current
state-of-the-art mobile CNNs, and further improves search efficiency by
adopting progressive search (Liu et al. 2017). Experimental results on CIFAR-10
are poised to demonstrate the effectiveness of Pareto-optimal networks found by
DPP-Net, for three different devices: (1) a workstation with Titan X GPU, (2)
NVIDIA Jetson TX1 embedded system, and (3) mobile phone with ARM Cortex-A53.
Compared to CondenseNet and NASNet (Mobile), DPP-Net achieves better
performances: higher accuracy and shorter inference time on various devices.
Additional experimental results show that models found by DPP-Net also achieve
considerably-good performance on ImageNet as well.
