TY - JOUR
T1 - FPGAaaS
T2 - A Survey of Infrastructures and Systems
AU - Al Qassem, Lamees M.
AU - Stouraitis, Thanos
AU - Damiani, Ernesto
AU - Elfadel, Ibrahim M.
N1 - Publisher Copyright:
© 2008-2012 IEEE.
PY - 2022
Y1 - 2022
N2 - The popularity of cloud computing services for delivering and accessing infrastructure on demand has significantly increased over the last few years. Concurrently, the usage of FPGAs to accelerate compute-intensive applications has become more widespread in different computational domains due to their ability to achieve high throughput and predictable latency while providing programmability and improved energy efficiency. Computationally intensive applications such as big data analytics, machine learning, and video processing have been accelerated by FPGAs. With the exponential workload increase in data centers, major cloud service providers have made FPGAs and their capabilities available as cloud services. However, enabling FPGAs in the cloud is not a trivial task due to incompatibilities with existing cloud infrastructure and operational challenges related to abstraction, virtualization, partitioning, and security. In this article, we survey recent frameworks for offering FPGA hardware acceleration as a cloud service, classify them based on their virtualization mode, tenancy model, communication interface, software stack, and hardware infrastructure. We further highlight current FPGAaaS trends and identify FPGA resource sharing, security, and microservicing as important areas for future research.
AB - The popularity of cloud computing services for delivering and accessing infrastructure on demand has significantly increased over the last few years. Concurrently, the usage of FPGAs to accelerate compute-intensive applications has become more widespread in different computational domains due to their ability to achieve high throughput and predictable latency while providing programmability and improved energy efficiency. Computationally intensive applications such as big data analytics, machine learning, and video processing have been accelerated by FPGAs. With the exponential workload increase in data centers, major cloud service providers have made FPGAs and their capabilities available as cloud services. However, enabling FPGAs in the cloud is not a trivial task due to incompatibilities with existing cloud infrastructure and operational challenges related to abstraction, virtualization, partitioning, and security. In this article, we survey recent frameworks for offering FPGA hardware acceleration as a cloud service, classify them based on their virtualization mode, tenancy model, communication interface, software stack, and hardware infrastructure. We further highlight current FPGAaaS trends and identify FPGA resource sharing, security, and microservicing as important areas for future research.
KW - FPGA
KW - hardware acceleration
KW - microservices
KW - orchestration
KW - virtualization
KW - Web services
UR - http://www.scopus.com/inward/record.url?scp=85128454127&partnerID=8YFLogxK
U2 - 10.1109/TSC.2020.2976012
DO - 10.1109/TSC.2020.2976012
M3 - Article
AN - SCOPUS:85128454127
SN - 1939-1374
VL - 15
SP - 1143
EP - 1156
JO - IEEE Transactions on Services Computing
JF - IEEE Transactions on Services Computing
IS - 2
ER -