SYSTEMATIC WEIGHT PRUNING OF DNNS USING ALTERNATING DIRECTION METHOD OF MULTIPLIERS

Tianyun Zhang, Shaokai Ye, Yipeng Zhang, Yanzhi Wang, Makan Fardad

Research output: Contribution to conferencePaperpeer-review

11 Scopus citations

Abstract

We present a systematic weight pruning framework of deep neural networks (DNNs) using the alternating direction method of multipliers (ADMM). We first formulate the weight pruning problem of DNNs as a constrained nonconvex optimization problem, and then adopt the ADMM framework for systematic weight pruning. We show that ADMM is highly suitable for weight pruning due to the computational efficiency it offers. We achieve a much higher compression ratio compared with prior work while maintaining the same test accuracy, together with a faster convergence rate.

Original languageEnglish (US)
StatePublished - 2018
Event6th International Conference on Learning Representations, ICLR 2018 - Vancouver, Canada
Duration: Apr 30 2018May 3 2018

Conference

Conference6th International Conference on Learning Representations, ICLR 2018
Country/TerritoryCanada
CityVancouver
Period4/30/185/3/18

ASJC Scopus subject areas

  • Education
  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics

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