S2DNAS: Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search

By Zhihang Yuan, Bingzhe Wu, Zheng Liang, Shiwan Zhao, Weichen Bi, Guangyu Sun. 2019

33 citations recorded by the source corpus.

Recently, dynamic inference has emerged as a promising way to reduce the computational cost of deep convolutional neural network (CNN). In contrast to static methods (e.g. weight pruning), dynamic inference adaptively adjusts the inference process according to each input sample, which can considerably reduce the computational cost on "easy" samples while maintaining the overall model performance. In this paper, we introduce a general framework, S2DNAS, which can transform various static CNN models to support dynamic inference via neural architecture search. To this end, based on a given CNN model, we first generate a CNN architecture space in which each architecture is a multi-stage CNN generated from the given model using some predefined transformations. Then, we propose a reinforcement learning based approach to automatically search for the optimal CNN architecture in the generated space. At last, with the searched multi-stage network, we can perform dynamic inference by adaptively choosing a stage to evaluate for each sample. Unlike previous works that introduce irregular computations or complex controllers in the inference or re-design a CNN model from scratch, our method can generalize to most of the popular CNN architectures and the searched dynamic network can be directly deployed using existing deep learning frameworks in various hardware devices.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Efficiency & Compression, Neural Architecture Search & AutoML

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Shiji Song
  2. Yulin Wang
  3. Gao Huang
  4. Le Yang
  5. Yizeng Han
  6. Honghui Wang
  7. Quoc V. Le
  8. Alan L. Yuille

In Computer Vision and Pattern Recognition

  1. Shiji Song
  2. Yulin Wang
  3. Gao Huang
  4. Yizeng Han
  5. Le Yang
  6. Honghui Wang
  7. Luc Van Gool
  8. Alan L. Yuille

In Efficiency & Compression

  1. Shiji Song
  2. Gao Huang
  3. Yulin Wang
  4. Le Yang
  5. Yizeng Han
  6. Honghui Wang

Citations in the corpus