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Collection of recent methods on DNN compression and acceleration
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LICENSE Initial commit Sep 16, 2018 Update Aug 1, 2019
references.bib update Jul 11, 2019


A collection of recent methods on DNN compression and acceleration. There are mainly 5 kinds of methods for efficient DNNs:

  • neural architecture re-designing or searching
    • maintain accuracy, less cost (e.g., #Params, #FLOPs, etc.): MobileNet, ShuffleNet etc.
    • maintain cost, more accuracy: Inception, ResNeXt, Xception etc.
  • pruning (including structured and unstructured)
  • quantization
  • matrix decomposition
  • knowledge distillation

About abbreviation: In the list below, o for oral, w for workshop, s for spotlight, b for best paper.


Papers-Advesarial Attacks


Papers-Knowledge Distillation

People (in alphabeta order)


Lightweight DNN Engines/APIs

Related Repos and Websites


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