The evolution of MobileNets has laid a solid foundation for neural network
applications on mobile end. With the latest MobileNetV3, neural architecture
search again claimed its supremacy in network design. Unfortunately, till today
all mobile methods mainly focus on CPU latencies instead of GPU, the latter,
however, is much preferred in practice for it has faster speed, lower overhead
and less interference. Bearing the target hardware in mind, we propose the
first Mobile GPU-Aware (MoGA) neural architecture search in order to be
precisely tailored for real-world applications. Further, the ultimate objective
to devise a mobile network lies in achieving better performance by maximizing
the utilization of bounded resources. Urging higher capability while
restraining time consumption is not reconcilable. We alleviate the tension by
weighted evolution techniques. Moreover, we encourage increasing the number of
parameters for higher representational power. With 200x fewer GPU days than
MnasNet, we obtain a series of models that outperform MobileNetV3 under the
similar latency constraints, i.e., MoGA-A achieves 75.9% top-1 accuracy on
ImageNet, MoGA-B meets 75.5% which costs only 0.5 ms more on mobile GPU. MoGA-C
best attests GPU-awareness by reaching 75.3% and being slower on CPU but faster
on GPU.The models and test code is made available here
this https URL

本文提出了 MoGA 神经架构搜索方法，通过 GPU-Aware 技术和加权进化技术，针对实际应用中移动终端 GPU 的特点进行优化，从而达到更好的性能。在类似延迟约束下，MoGA-A 在 ImageNet 上实现了 75.9％的 Top-1 精度，其他模型也表现优异。