@inproceedings{39aefe7fd879435aac3debeb9daa557a,
title = "Automatic memory-efficient scheduling of CNNs",
abstract = "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.",
keywords = "CNN, Memory efficient, Reuse, Scheduling",
author = "Luc Waeijen and Savvas Sioutas and Yifan He and Maurice Peemen and Henk Corporaal",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-27562-4_28",
language = "English",
isbn = "978-3-030-27561-7",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "387--400",
editor = "Maxime Pelcat and Matthias Jung and Pnevmatikatos, {Dionisios N.}",
booktitle = "Embedded Computer Systems",
address = "Germany",
note = "19th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2019 ; Conference date: 07-07-2019 Through 11-07-2019",
}