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1. The Future of FPGA Acceleration in Datacenters and the Cloud

   Christophe Bobda; Joel Mandebi Mbongue; Paul Chow; Mohammad Ewais; Naif Tarafdar; Juan Camilo Vega; Ken Eguro; Dirk Koch; Suranga Handagala; Miriam Leeser; et al (January 2022, ACM transactions on reconfigurable technology and systems)

   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. 

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2. FaaM: FPGA-as-a-Microservice - A Case Study for Data Compression

   https://doi.org/10.1051/epjconf/201921407029  

   Ojika, David; Gordon-Ross, Ann; Lam, Herman; Patel, Bhavesh; Forti, A.; Betev, L.; Litmaath, M.; Smirnova, O.; Hristov, P. (January 2019, EPJ Web of Conferences)

   Field-programmable gate arrays (FPGAs) have largely been used in communication and high-performance computing and given the recent advances in big data and emerging trends in cloud computing (e.g., serverless [18]), FPGAs are increasingly being introduced into these domains (e.g., Microsoft’s datacenters [6] and Amazon Web Services [10]). To address these domains’ processing needs, recent research has focused on using FPGAs to accelerate workloads, ranging from analytics and machine learning to databases and network function virtualization. In this paper, we present an ongoing effort to realize a high-performance FPGA-as-a-microservice (FaaM) architecture for the cloud. We discuss some of the technical challenges and propose several solutions for efficiently integrating FPGAs into virtualized environments. Our case study deploying a multithreaded, multi-user compression as a microservice using the FaaM architecture indicate that microservices-based FPGA acceleration can sustain high-performance compared to straightforward implementation with minimal to no communication overhead despite the hardware abstraction. 

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3. Using FPGAs as Microservices: Technology, Challenges and Case Study

   Ojika, S; Gordon-Ross, A; Lam, H; Patel, B; Kaul, G; Strayer, J (March 2018, 9th Workshop on Big Data Benchmarks Performance, Optimization and Emerging Hardware (BPOE-9))

   Field-programmable gate arrays (FPGAs) have largely been used in communication and high-performance computing, and given the recent advances in big data, machine learning and emerging trends in cloud computing (e.g., serverless [1]), FPGAs are increasingly being introduced into these domains (e.g., Microsoft’s datacenters [2] and Amazon Web Services [3]). To address these domains’ processing needs, recent research has focused on using FPGAs to accelerate workloads, ranging from analytics and machine learning to databases and network function virtualization. In this paper, we present a high-performance FPGA-as-a-microservice (FaaM) architecture for the cloud. We discuss some of the technical challenges and propose several solutions for efficiently integrating FPGAs into virtualized environments. Our case study deploying a multi-threaded, multi-user compression as a microservice using FaaM indicates that microservices-based FPGA acceleration can sustain high-performance as compared to a straightforward CPU implementation with minimal to no communication overhead despite the hardware abstraction. 

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4. Multi-Tenant Cloud FPGA: A Survey on Security, Trust and Privacy

   https://doi.org/10.1145/3713078  

   Kawser_Ahmed, Muhammed; Panoff_Kealoha, Maximillian; Mandebi_Mbongue, Joel; Saha, Sujan Kumar; Nghonda_Tchinda, Erman; Mbua, Peter Esenju; Bobda, Christophe (January 2025, ACM Transactions on Reconfigurable Technology and Systems)

   With the growing demand for enhanced performance and scalability in cloud applications and systems, data center architectures are evolving to incorporate heterogeneous computing fabrics that leverage CPUs, GPUs, and FPGAs. Unlike traditional processing platforms like CPUs and GPUs, FPGAs offer the unique ability for hardware reconfiguration at run-time, enabling improved and tailored performance, flexibility, and acceleration. FPGAs excel at executing large-scale search optimization, acceleration, and signal processing tasks while consuming low power and minimizing latency. Major public cloud providers, such as Amazon, Huawei, Microsoft, Alibaba, and others, have already begun integrating FPGA-based cloud acceleration services into their offerings. Although FPGAs in cloud applications facilitate customized hardware acceleration, they also introduce new security challenges that demand attention. Granting cloud users the capability to reconfigure hardware designs after deployment may create potential vulnerabilities for malicious users, thereby jeopardizing entire cloud platforms. In particular, multi-tenant FPGA services, where a single FPGA is divided spatially among multiple users, are highly vulnerable to such attacks. This paper examines the security concerns associated with multi-tenant cloud FPGAs, provides a comprehensive overview of the related security, privacy and trust issues, and discusses forthcoming challenges in this evolving field of study. 

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5. Deploying Multi-tenant FPGAs within Linux-based Cloud Infrastructure

   https://doi.org/10.1145/3474058  

   Mbongue, Joel Mandebi; Kwadjo, Danielle Tchuinkou; Shuping, Alex; Bobda, Christophe (June 2022, ACM Transactions on Reconfigurable Technology and Systems)

   Cloud deployments now increasingly exploit Field-Programmable Gate Array (FPGA) accelerators as part of virtual instances. While cloud FPGAs are still essentially single-tenant, the growing demand for efficient hardware acceleration paves the way to FPGA multi-tenancy. It then becomes necessary to explore architectures, design flows, and resource management features that aim at exposing multi-tenant FPGAs to the cloud users. In this article, we discuss a hardware/software architecture that supports provisioning space-shared FPGAs in Kernel-based Virtual Machine (KVM) clouds. The proposed hardware/software architecture introduces an FPGA organization that improves hardware consolidation and support hardware elasticity with minimal data movement overhead. It also relies on VirtIO to decrease communication latency between hardware and software domains. Prototyping the proposed architecture with a Virtex UltraScale+ FPGA demonstrated near specification maximum frequency for on-chip data movement and high throughput in virtual instance access to hardware accelerators. We demonstrate similar performance compared to single-tenant deployment while increasing FPGA utilization, which is one of the goals of virtualization. Overall, our FPGA design achieved about 2× higher maximum frequency than the state of the art and a bandwidth reaching up to 28 Gbps on 32-bit data width. 

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