Zobrazeno 1 - 10
of 291
pro vyhledávání: '"Hans Vandierendonck"'
Publikováno v:
IEEE Access, Vol 12, Pp 5490-5502 (2024)
Many weight quantization approaches were explored to save the communication bandwidth between the clients and the server in federated learning using high-end computing machines. However, there is a lack of weight quantization research for online fede
Externí odkaz:
https://doaj.org/article/d7f3178eb1934215955a42380cb29836
Publikováno v:
IEEE Access, Vol 10, Pp 69543-69554 (2022)
The past decade has shown a surge in the use and application of machine learning and deep learning models across various domains. One such domain is credit scoring, where applicants are scored to assess their creditworthiness for loan applications. I
Externí odkaz:
https://doaj.org/article/39e03a8703404d4bb00909810046ba7a
Autor:
Umar Ibrahim Minhas, JunKyu Lee, Lev Mukhanov, Georgios Karakonstantis, Hans Vandierendonck, Roger Woods
Publikováno v:
Minhas, U, Lee, J, Mukhanov, L, Karakonstantis, G, Vandierendonck, H & Woods, R 2022, ' Increased Leverage of Transprecision Computing for Machine Vision Applications at the Edge ', Journal of Signal Processing Systems, vol. 94, pp. 1101-1118 .
The practical deployment of machine vision presents particular challenges for resource constrained edge devices. With a clear need to execute multiple tasks with variable workloads, there is a need for a robust approach that can dynamically adapt at
Publikováno v:
Lee, J, Nikolopoulos, D S & Vandierendonck, H 2020, ' Mixed-Precision Kernel Recursive Least Squares ', IEEE Transactions on Neural Networks and Learning Systems . https://doi.org/10.1109/TNNLS.2020.3041677
Kernel recursive least squares (KRLS) is a widely used online machine learning algorithm for time series predictions. In this article, we present the mixed-precision KRLS, producing equivalent prediction accuracy to double-precision KRLS with a highe
Publikováno v:
Lee, J, Varghese, B & Vandierendonck, H 2023, ROMA: run-time object detection to maximize real-time accuracy . in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023 . IEEE/CVF Winter Conference on Applications of Computer Vision (WACV): Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 6394-6403, IEEE Winter Conference on Applications of Computer Vision, Waikoloa, Hawaii, United States, 02/01/2023 . https://doi.org/10.1109/wacv56688.2023.00634
This paper analyzes the effects of dynamically varying video contents and detection latency on the real-time detection accuracy of a detector and proposes a new run-time accuracy variation model, ROMA, based on the findings from the analysis. ROMA is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::139b6ede175c3fd4d1183e22eeae91ed
https://pure.qub.ac.uk/en/publications/17d72226-40c7-426c-a108-1332915983b4
https://pure.qub.ac.uk/en/publications/17d72226-40c7-426c-a108-1332915983b4
Autor:
Konstantinos Tovletoglou, Georgios Karakonstantis, Dimitrios S. Nikolopoulos, Lev Mukhanov, Hans Vandierendonck
Publikováno v:
Mukhanov, L, Tovletoglou, K, Vandierendonck, H, Nikolopoulos, D & Karakonstantis, G 2020, ' Revealing DRAM Operating GuardBands through Workload-Aware Error Predictive Modeling ', IEEE Transactions on Computers . https://doi.org/10.1109/TC.2020.3033627
Improving the energy efficiency of DRAMs becomes very challenging due to the growing demand for storage capacity and failures induced by the manufacturing process. To protect against failures, vendors adopt conservative margins in the refresh period
Publikováno v:
Koohi Esfahani, M, Kilpatrick, P & Vandierendonck, H 2022, MASTIFF: structure-aware minimum spanning tree/forest . in Proceedings of the 36th ACM International Conference on Supercomputing, ICS 2022 ., 9, ACM International Conference on Supercomputing: Proceedings, Association for Computing Machinery, 36th ACM International Conference on Supercomputing, virtual, online, 28/06/2022 . https://doi.org/10.1145/3524059.3532365
The Minimum Spanning Forest (MSF) problem finds usage in many different applications. While theoretical analysis shows that linear-time solutions exist, in practice, parallel MSF algorithms remain computationally demanding due to the continuously inc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d3c91aba25805a40f130055033736f7f
https://pure.qub.ac.uk/en/publications/456f85fe-6f36-4d50-a61d-2ee0feb97265
https://pure.qub.ac.uk/en/publications/456f85fe-6f36-4d50-a61d-2ee0feb97265
Autor:
Hans Vandierendonck
Publikováno v:
Proceedings of the 36th ACM International Conference on Supercomputing.
Publikováno v:
2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS).
Publikováno v:
Koohi Esfahani, M, Kilpatrick, P & Vandierendonck, H 2022, LOTUS: Locality Optimizing Triangle Counting . in 27th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP 2022): Proceedings . Association for Computing Machinery, pp. 219-233 . https://doi.org/10.1145/3503221.3508402
Triangle Counting (TC) is a basic graph mining problem with numerous applications. However, the large size of real-world graphs has a severe effect on TC performance.This paper studies the TC algorithm from the perspective of memory utilization. We i