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 runtime, 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 article 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.

The increasing adoption of Field-Programmable Gate Arrays (FPGA) into cloud and data center systems opens the way to the unprecedented acceleration of Machine Learning applications. Convolutional Neural Networks (CNN) have largely been adopted as algorithms for image classification and object detection. As we head towards FPGA multi-tenancy in the cloud, it becomes necessary to investigate architectures and mechanisms for the efficient deployment of CNN into multitenant FPGAs cloud Infrastructure. In this work, we propose an FPGA architecture and a design flow that support efficient integration of CNN applications into a cloud infrastructure that exposes multi-tenancy to cloud developers. We prototype the proposed approach on randomly allocated virtual regions to tenants. We study how space-sharing of a single device between multiple cloud tenants influence the design flow, the allocation of resources, and the performance in term of resource utilization and overall latency compared to single-tenant deployments. Prototyping results show a latency at most 8% lower than that of single-tenant deployment while achieving higher resource utilization. We also record a maximum frequency of up to 12% higher in multi-tenant implementations.

With the vast adoption of FPGAs in the cloud, it becomes necessary to investigate architectures and mechanisms for the efficient deployment of CNN into multi-FPGAs cloud Infrastructure. However, neural networks’ growing size and complexity, coupled with communication and off-chip memory bottlenecks, make it increasingly difficult for multi-FPGA designs to achieve high resource utilization. In this work, we introduce a scalable framework that supports the efficient integration of CNN applications into a cloud infrastructure that exposes multi-Die FPGAs to cloud developers. Our framework is equipped is with two mechanisms to facilitate the deployment of CNN inference on FPGA. First, we propose a model to find the parameters that maximize the parallelism within the resource budget while maintaining a balanced rate between the layers. Then, we propose an efficient Coarse-Grained graph partitioning algorithm for high-quality and scalable routability-drive placement of CNN’s components on the FPGAs. Prototyping results achieve an overall 37% higher frequency, with lower resource usage compared to a baseline implementation on the same number of FPGAs.