{rfName}
Co

Indexed in

License and use

Icono OpenAccess

Citations

2

Altmetrics

Analysis of institutional authors

Alonso P.AuthorAlonso, PAuthorQuintana-Ortí, EsAuthor

Share

Publications
>
Proceedings Paper

Convolution Operators for Deep Learning Inference on the Fujitsu A64FX Processor

Publicated to:Proceedings - Symposium On Computer Architecture And High Performance Computing. 160-169 - 2022-01-01 (), DOI: 10.1109/SBAC-PAD55451.2022.00027

Authors: Dolz, MF; Martinez, H; Alonso, P; Quintana-Ortí, ES

Affiliations

Univ Cordoba - Author
Univ Jaume I Castellon - Author
Univ Politecn Valencia - Author

Abstract

The convolution operator is a crucial kernel for many computer vision and signal processing applications that rely on deep learning (DL) technologies. As such, the efficient implementation of this operator has received considerable attention in the past few years for a fair range of processor architectures. In this paper, we follow the technology trend toward integrating long SIMD (single instruction, multiple data) arithmetic units into high performance multicore processors to analyse the benefits of this type of hardware acceleration for latency-constrained DL workloads. For this purpose, we implement and optimise for the Fujitsu processor A64FX, three distinct methods for the calculation of the convolution, namely, the lowering approach, a blocked variant of the direct convolution algorithm, and the Winograd minimal filtering algorithm. Our experimental results include an extensive evaluation of the parallel scalability of these three methods and a comparison of their global performance using three popular DL models and a representative dataset.

Keywords

Arm-based a64fx processorConvolutional neural networksHigh performanceSimd arithmetic units

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 2.27, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Jun 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-06-20, the following number of citations:

  • Scopus: 2
  • OpenCitations: 3

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-20:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 3 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author (Quintana Ortí, Enrique Salvador).