SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models
This work addresses the challenge of model efficiency for edge device deployment, though it appears incremental as it builds upon existing acceleration and compression methods.
The paper tackles the problem of deploying deep neural networks on resource-limited edge devices by proposing the SCAN framework, which divides networks into sections with shallow classifiers and uses attention and knowledge distillation to enhance accuracy, achieving significant performance gains on CIFAR100 and ImageNet without hyper-parameter adjustments.
Remarkable achievements have been attained by deep neural networks in various applications. However, the increasing depth and width of such models also lead to explosive growth in both storage and computation, which has restricted the deployment of deep neural networks on resource-limited edge devices. To address this problem, we propose the so-called SCAN framework for networks training and inference, which is orthogonal and complementary to existing acceleration and compression methods. The proposed SCAN firstly divides neural networks into multiple sections according to their depth and constructs shallow classifiers upon the intermediate features of different sections. Moreover, attention modules and knowledge distillation are utilized to enhance the accuracy of shallow classifiers. Based on this architecture, we further propose a threshold controlled scalable inference mechanism to approach human-like sample-specific inference. Experimental results show that SCAN can be easily equipped on various neural networks without any adjustment on hyper-parameters or neural networks architectures, yielding significant performance gain on CIFAR100 and ImageNet. Codes will be released on github soon.