Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference

Yue Wang, Jianghao Shen, Ting Kuei Hu, Pengfei Xu, Tan Nguyen, Richard Baraniuk, Zhangyang Wang, Yingyan Lin

Research output: Contribution to journalArticlepeer-review

53 Scopus citations


State-of-the-art convolutional neural networks (CNNs) yield record-breaking predictive performance, yet at the cost of high-energy-consumption inference, that prohibits their widely deployments in resource-constrained Internet of Things (IoT) applications. We propose a dual dynamic inference (DDI) framework that highlights the following aspects: 1) we integrate both input-dependent and resource-dependent dynamic inference mechanisms under a unified framework in order to fit the varying IoT resource requirements in practice. DDI is able to both constantly suppress unnecessary costs for easy samples, and to halt inference for all samples to meet hard resource constraints enforced; 2) we propose a flexible multi-grained learning to skip (MGL2S) approach for input-dependent inference which allows simultaneous layer-wise and channel-wise skipping; 3) we extend DDI to complex CNN backbones such as DenseNet and show that DDI can be applied towards optimizing any specific resource goals including inference latency and energy cost. Extensive experiments demonstrate the superior inference accuracy-resource trade-off achieved by DDI, as well as the flexibility to control such a trade-off as compared to existing peer methods. Specifically, DDI can achieve up to 4 times computational savings with the same or even higher accuracy as compared to existing competitive baselines.

Original languageEnglish (US)
Article number9028245
Pages (from-to)623-633
Number of pages11
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number4
StatePublished - May 2020


  • Dynamic inference
  • input-dependent
  • multi-grained
  • resource-dependent

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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