ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices (1707.01083v2)

Abstract: We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet on ImageNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves ~13x actual speedup over AlexNet while maintaining comparable accuracy.

• The paper presents an innovative CNN design that combines pointwise group convolutions with channel shuffle to reduce computational cost while maintaining accuracy.
• Experimental results show a 7.8% lower top-1 error at 40 MFLOPs and a ~13× runtime speedup over AlexNet on ARM-based devices.
• The study highlights practical implications for deploying efficient deep learning models on mobile and embedded systems, paving the way for future advancements.

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

The paper "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices" introduces a convolutional neural network (CNN) architecture, ShuffleNet, specifically designed to operate efficiently within the stringent computational constraints of mobile devices (10-150 MFLOPs). This design is achieved through two primary innovations: pointwise group convolution and channel shuffle. These techniques collectively reduce computational resource usage while preserving or improving the model accuracy.

Innovation in Architecture

The ShuffleNet architecture addresses the challenge of high computational costs associated with dense $1\times 1$ convolutions in state-of-the-art neural networks like Xception and ResNeXt. To maintain computational efficiency, ShuffleNet employs pointwise group convolutions, effectively reducing the amount of computation required by spreading the convolutional operations over multiple groups. However, this approach isolates groups of channels from interacting with each other, which can negatively impact the flow of information between groups.

To mitigate this isolation issue, the paper introduces the channel shuffle operation. This operation reorganizes the channels between groups, allowing for cross-group information exchange, thereby enhancing the network's representational capacity without significant computational overhead. By interweaving pointwise group convolutions with channel shuffle, ShuffleNet achieves a balance between efficiency and performance.

Experimental Validation

Extensive experimentation on the ImageNet classification task underscores the effectiveness of ShuffleNet. For a computational budget of 40 MFLOPs, ShuffleNet achieves an absolute 7.8% lower top-1 error compared to the leading MobileNet architecture. This significant improvement showcases the superior performance of ShuffleNet for efficient neural computing.

In terms of real-world application, the ShuffleNet architecture achieves remarkable speedup on ARM-based mobile devices. Specifically, ShuffleNet provides a $\sim$13$\times$ actual runtime speedup over AlexNet while maintaining comparable accuracy, validating its practical application for mobile and embedded systems.

Comparative Analysis

ShuffleNet's efficacy was further validated against other prominent architectures, including VGG-like, ResNet, Xception-like, and ResNeXt structures. For a given computational budget, ShuffleNet consistently outperformed these architectures, particularly excelling in scenarios with smaller networks where computational resources are critically constrained.

Practical Implications and Future Directions

ShuffleNet holds significant practical implications for deploying deep learning models on mobile and edge devices, where computational efficiency is paramount. Its innovative design can drive advancements in various applications, from object recognition in robotics to real-time image processing on smartphones.

Looking ahead, the incorporation of latest advancements such as SE blocks has already shown potential for further enhancing ShuffleNet's performance, suggesting that continued integration of novel architectural innovations could yield even more robust and efficient models. Additionally, the paper sparks potential for further explorations in automated neural architecture search tailored specifically for resource-constrained environments.

Conclusion

In conclusion, the ShuffleNet architecture represents an important step forward in the design of efficient, high-performance CNNs suitable for mobile devices. By effectively leveraging pointwise group convolutions and channel shuffle operations, ShuffleNet manages to reduce computational costs significantly while still delivering strong numerical performance, providing a valuable architecture for the future of mobile AI applications.