Designing Energy-Efficient Convolutional Neural Networks
using Energy-Aware Pruning

Email: dnn-energy at mit dot edu

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  • 03/25/2017

    Energy estimation website goes live!


Deep convolutional neural networks (CNNs) are indispensable to state-of-the-art computer vision algorithms. However, they are still rarely deployed on battery-powered mobile devices, such as smartphones and wearable gadgets, where vision algorithms can enable many revolutionary real-world applications. The key limiting factor is the high energy consumption of CNN processing due to its high computational complexity. While there are many previous efforts that try to reduce the CNN model size or amount of computation, we find that they do not necessarily result in lower energy consumption, and therefore do not serve as a good metric for energy cost estimation.

To close the gap between CNN design and energy consumption optimization, we propose an energy-aware pruning algorithm for CNNs that directly uses energy consumption estimation of a CNN to guide the pruning process. The energy estimation methodology uses parameters extrapolated from actual hardware measurements that target realistic battery-powered system setups. The proposed layer-by-layer pruning algorithm also prunes more aggressively than previously proposed pruning methods by minimizing the error in the output feature maps instead of the filter weights. For each layer, the weights are first pruned and then locally fine-tuned with a closed-form least-square solution to quickly restore the accuracy. After all layers are pruned, the entire network is further globally fine-tuned using back-propagation. With the proposed pruning method, the energy consumption of AlexNet and GoogLeNet are reduced by 3.7x and 1.6x, respectively, with less than 1% top-5 accuracy loss. Finally, we show that pruning the AlexNet with a reduced number of target classes can greatly decrease the number of weights but the energy reduction is limited.

Energy Estimation Methodology

Energy Estimation Methodology

Energy vs Accuracy

Energy versus Accuracy Tradeoff

Energy Estimation Tool


    author      = {{Yang, Tien-Ju and Chen, Yu-Hsin and Sze, Vivienne}},
    title       = {{Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning}},
    booktitle   = {{IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}},
    year        = {{2017}},

Related Papers

  • V. Sze, T.-J. Yang, Y.-H. Chen, J. Emer, "Efficient Processing of Deep Neural Networks: A Tutorial and Survey," arXiv, 2017. [ preprint arXiv ]
  • T.-J. Yang, Y.-H. Chen, V. Sze, "Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning," to appear at IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017. [ preprint arXiv ]
  • Y.-H. Chen, J. Emer, V. Sze, "Using Dataflow to Optimize Energy Efficiency of Deep Neural Network Accelerators," to appear in IEEE Micro's Top Picks from the Computer Architecture Conferences, May/June 2017.
  • A. Suleiman*, Y.-H. Chen*, J. Emer, V. Sze, "Towards Closing the Energy Gap Between HOG and CNN Features for Embedded Vision," to appear at IEEE International Symposium of Circuits and Systems (ISCAS), Invited Paper, May 2017. [ PDF ]
  • V. Sze, Y.-H. Chen, J. Emer, A. Suleiman, Z. Zhang, "Hardware for Machine Learning: Challenges and Opportunities," to appear at IEEE Custom Integrated Circuits Conference (CICC), Invited Paper, May 2017. [ preprint arXiv ]
  • Y.-H. Chen, T. Krishna, J. Emer, V. Sze, "Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks," IEEE Journal of Solid State Circuits (JSSC), ISSCC Special Issue, Vol. 52, No. 1, pp. 127-138, January 2017. [ PDF ]
  • Y.-H. Chen, J. Emer, V. Sze, "Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks," International Symposium on Computer Architecture (ISCA), pp. 367-379, June 2016. [ paper PDF | slides PDF ] Selected for IEEE Micro’s Top Picks special issue on "most significant papers in computer architecture based on novelty and long-term impact" from 2016
  • Y.-H. Chen, T. Krishna, J. Emer, V. Sze, "Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks," IEEE International Conference on Solid-State Circuits (ISSCC), pp. 262-264, February 2016. [ paper PDF | slides PDF | poster PDF | demo video | project website ] Highlighted in EETimes and MIT News.
* Indicates authors contributed equally to the work

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