Publications
Reformulating the direct convolution for high-performance deep learning inference on ARM processors
TYPE OF PUBLICATION
AUTHORS
Sergio Barrachina, Adrián Castelló, Manuel F. Dolz, Tze Meng Low, Héctor Martínez, Enrique S. Quintana-Ortí, Upasana Sridhar, Andrés E. Tomás,
PUBLISHER
Journal of Systems Architecture
YEAR OF PUBLICATION
2023
PLACE OF PUBLICATION
ISSN
ISSN 1383-7621
DOI
https://doi.org/10.1016/j.sysarc.2022.102806
CITACION
Sergio Barrachina, Adrián Castelló, Manuel F. Dolz, Tze Meng Low, Héctor Martínez, Enrique S. Quintana-Ortí, Upasana Sridhar, Andrés E. Tomás,
Reformulating the direct convolution for high-performance deep learning inference on ARM processors,
Journal of Systems Architecture,
Volume 135,
2023,
102806,
ISSN 1383-7621,
https://doi.org/10.1016/j.sysarc.2022.102806.
(https://www.sciencedirect.com/science/article/pii/S1383762122002910)
Abstract: We present two high-performance implementations of the convolution operator via the direct algorithm that outperform the so-called lowering approach based on the im2col transform plus the gemm kernel on an ARMv8-based processor. One of our methods presents the additional advantage of zero-memory overhead while the other employs an additional yet rather moderate workspace, substantially smaller than that required by the im2col+gemm solution. In contrast with a previous implementation of a similar zero-memory overhead direct convolution, this work exhibits the key advantage of preserving the conventional NHWC data layout for the input/output activations of the convolution layers.
Keywords: Convolution; Direct algorithm; Deep learning; High performance; ARMv8 architecture
LINK TO THE REPOSITORY
https://www.sciencedirect.com/science/article/pii/S1383762122002910
LINK TO THE PUBLICATION
https://www.sciencedirect.com/science/article/pii/S1383762122002910