Memory Allocation for Neural Networks using Graph Coloring

Leonid Barenboim, Rami Drucker, Oleg Zatulovsky, Eli Levi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The Memory Allocation problem for neural networks can be represented as a two-dimensional optimization problem. The neural network is allocated into limited memory space while allocating as much data as possible into the low latency memory. Our solution is based on a generalization of graph coloring, edge-to-node transformation and considers the order in which the graph nodes are colored. We observed improvement of more than 40% in SRAM memory bandwidth in various neural networks.

Original languageEnglish
Title of host publicationICDCN 2022 - Proceedings of the 2022 International Conference on Distributed Computing and Networking
Pages232-233
Number of pages2
ISBN (Electronic)9781450395601
DOIs
StatePublished - 4 Jan 2022
Event23rd International Conference on Distributed Computing and Networking, ICDCN 2022 - Virtual, Online, India
Duration: 4 Jan 20227 Jan 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference23rd International Conference on Distributed Computing and Networking, ICDCN 2022
Country/TerritoryIndia
CityVirtual, Online
Period4/01/227/01/22

Keywords

  • Graph Coloring
  • Memory Management
  • Neural Networks

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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