KnowledgeNet: Disaggregated and distributed training and serving of deep neural networks

Saman Biookaghazadeh, Yitao Chen, Kaiqi Zhao, Ming Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Deep Neural Networks (DNNs) have a significant impact on numerous applications, such as video processing, virtual/augmented reality, and text processing. The ever-changing environment forces the DNN models to evolve, accordingly. Also, the transition from the cloud-only to edge-cloud paradigm has made the deployment and training of these models challenging. Addressing these challenges requires new methods and systems for continuous training and distribution of these models in a heterogeneous environment. In this paper, we propose KnowledgeNet (KN), which is a new architectural technique for a simple disaggregation and distribution of the neural networks for both training and serving. Using KN, DNNs can be partitioned into multiple small blocks and be deployed on a distributed set of computational nodes. Also, KN utilizes the knowledge transfer technique to provide small scale models with high accuracy in edge scenarios with limited resources. Preliminary results show that our new method can ensure a state-of-the-art accuracy for a DNN model while being disaggregated among multiple workers. Also, by using knowledge transfer technique, we can compress the model by 62% for deployment, while maintaining the same accuracy.

Original languageEnglish (US)
Title of host publicationProceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019
PublisherUSENIX Association
Pages47-49
Number of pages3
ISBN (Electronic)9781939133007
StatePublished - 2019
Event2019 USENIX Conference on Operational Machine Learning, OpML 2019 - Santa Clara, United States
Duration: May 20 2019 → …

Publication series

NameProceedings of the 2019 USENIX Conference on Operational Machine Learning, OpML 2019

Conference

Conference2019 USENIX Conference on Operational Machine Learning, OpML 2019
CountryUnited States
CitySanta Clara
Period5/20/19 → …

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction

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