MoGA: Searching Beyond MobileNetV3

by   Xiangxiang Chu, et al.

The evolution of MobileNets has laid a solid foundation for neural network application on the mobile end. With the latest MobileNetV3, neural architecture search again claimed its supremacy on network design. Till today all mobile methods mainly focus on CPU latency instead of GPU, the latter, however, has lower overhead and interference and is much preferred in the industry. To mitigate this gap, 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. While urging higher capability and restraining time consumption, we unconventionally encourage increasing the number of parameters for higher representational power. Undoubtedly, these three forces are not reconcilable and we have to alleviate the tension by weighted evolution techniques. Lastly, we deliver our searched networks at a mobile scale 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 0.5ms more on mobile GPU than MobileNetV3, which scores 75.2%. 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


CARS: Continuous Evolution for Efficient Neural Architecture Search

Searching techniques in most of existing neural architecture search (NAS...

Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective

Neural Architecture Search (NAS) has been explosively studied to automat...

Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation

Recently, differentiable search methods have made major progress in redu...

LETI: Latency Estimation Tool and Investigation of Neural Networks inference on Mobile GPU

A lot of deep learning applications are desired to be run on mobile devi...

MnasNet: Platform-Aware Neural Architecture Search for Mobile

Designing convolutional neural networks (CNN) models for mobile devices ...

Neural Architecture Search for Lightweight Non-Local Networks

Non-Local (NL) blocks have been widely studied in various vision tasks. ...

NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications

This work proposes an automated algorithm, called NetAdapt, that adapts ...