The VINEYARD Integrated Framework for Hardware Accelerators in the Cloud

Christoforos Kachris, Dimitrios Soudris, Stelios Mavridis, Manolis Pavlidakis, Christi Symeonidou, Christos Kozanitis, Angelos Bilas, Damon Fenacci, Sharatchandra Varma Bogaraju, Hans Vandierendonck, Dimitrios S. Nikolopoulos

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


Emerging cloud applications like machine learning, AI and big data analytics required high performance computing systems that can sustain the increased amount of data processing without consuming excessive power. Towards this end, many cloud operators have started deploying hardware accelerators, like FPGAs, to increase the performance of computational intensive tasks but increasing the programming complexity to utilize these accelerators. VINEYARD has developed an efficient framework that allows the seamless deployment and utilization of hardware accelerators in the cloud without increasing the programming complexity and offering the flexibility of software packages. This paper presents the main components that have been developed in this framework such as the runtime system, the virtualization and the central acceleratorsâĂŹ repository. The proposed platform has been demonstrated into 2 real use cases; neurocomputing applications and machine learning algorithms. The performance evaluation shows that the proposed scheme can achieve up to 25x speedup without increasing the programming complexity of these applications and it also demonstrates how it can be used for large suite of applications.
Original languageEnglish
Title of host publicationProceedings of the 18th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Print)978-1-4503-6494-2
Publication statusPublished (in print/issue) - 15 Jul 2018

Publication series

NameSAMOS '18


  • FPGAs
  • cloud computing
  • hardware accelerators
  • reconfigurable computing
  • machine learning,


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