Automatic memory-efficient scheduling of CNNs

Luc Waeijen, Savvas Sioutas, Yifan He, Maurice Peemen, Henk Corporaal

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

1 Citation (Scopus)
15 Downloads (Pure)


Accessing large external DRAM is costly, and poses a challenge to efficiently evaluate data-intensive convolutional neural networks (CNNs) on embedded devices. These external memory accesses can be minimized by exploiting data reuse in on-chip memory. Selecting the combination of code transformations that minimize the external DRAM accesses is however an extremely complex task. In this work a mathematical model is presented to quickly and very precisely evaluate combinations of code transformations on CNNs. An accompanying open source tool is developed which leverages this model to perform automated design space exploration and code generation for CNNs. The correctness of the developed model is demonstrated by measurement of seven neural networks. Results show the transformations selected by the tool can reduce external memory accesses by over an order of magnitude.

Original languageEnglish
Title of host publicationEmbedded Computer Systems
Subtitle of host publicationArchitectures, Modeling, and Simulation - 19th International Conference, SAMOS 2019, Proceedings
EditorsMaxime Pelcat, Matthias Jung, Dionisios N. Pnevmatikatos
Place of PublicationCham
Number of pages14
ISBN (Electronic)978-3-030-27562-4
ISBN (Print)978-3-030-27561-7
Publication statusPublished - 1 Jan 2019
Event19th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2019 - Samos, Greece
Duration: 7 Jul 201911 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11733 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2019


  • CNN
  • Memory efficient
  • Reuse
  • Scheduling


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