The Future of FPGA Acceleration in Datacenters and the Cloud

Author:

Bobda Christophe1ORCID,Mbongue Joel Mandebi1ORCID,Chow Paul2ORCID,Ewais Mohammad2,Tarafdar Naif2ORCID,Vega Juan Camilo2,Eguro Ken3ORCID,Koch Dirk4ORCID,Handagala Suranga5,Leeser Miriam5ORCID,Herbordt Martin6,Shahzad Hafsah6,Hofste Peter7,Ringlein Burkhard8ORCID,Szefer Jakub9,Sanaullah Ahmed10,Tessier Russell11

Affiliation:

1. University of Florida, Gainesville, FL

2. University of Toronto, Toronto, Ontario, CANADA

3. Microsoft, Redmond, Washington, United States of America

4. Manchester University, Oxford Road, Manchester

5. Northeastern University, Boston, Massachusetts

6. Boston University, Saint Mary’s Street Boston, MA

7. IBM POWER Systems Performance, Burnet Rd Austin, TX

8. IBM Research Europe, Säumerstrasse, Rüschlikon, Switzerland

9. Yale University, Hillhouse Avenue New Haven, CT

10. Red Hat, Inc

11. University of Massachusetts Amherst, Natural Resources Road Amherst MA

Abstract

In this article, we survey existing academic and commercial efforts to provide Field-Programmable Gate Array (FPGA) acceleration in datacenters and the cloud. The goal is a critical review of existing systems and a discussion of their evolution from single workstations with PCI-attached FPGAs in the early days of reconfigurable computing to the integration of FPGA farms in large-scale computing infrastructures. From the lessons learned, we discuss the future of FPGAs in datacenters and the cloud and assess the challenges likely to be encountered along the way. The article explores current architectures and discusses scalability and abstractions supported by operating systems, middleware, and virtualization. Hardware and software security becomes critical when infrastructure is shared among tenants with disparate backgrounds. We review the vulnerabilities of current systems and possible attack scenarios and discuss mitigation strategies, some of which impact FPGA architecture and technology. The viability of these architectures for popular applications is reviewed, with a particular focus on deep learning and scientific computing. This work draws from workshop discussions, panel sessions including the participation of experts in the reconfigurable computing field, and private discussions among these experts. These interactions have harmonized the terminology, taxonomy, and the important topics covered in this manuscript.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

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