Abstract. 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 devicerelated objectives is immensely crucial for deploying deep networks on
portable devices with limited computing resources. We propose DPPNet: 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 & shorter inference time on various devices. Additional experimental
results show that models found by DPP-Net also achieve considerablygood performance on ImageNet as well