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1. Architecture Support for FPGA Multi-tenancy in the Cloud

   https://doi.org/10.1109/ASAP49362.2020.00030  

   Mbongue, Joel Mandebi; Shuping, Alex; Bhowmik, Pankaj; Bobda, Christophe (July 2020, 2020 IEEE 31st International Conference on Application-specific Systems, Architectures and Processors (ASAP))

   null (Ed.)

   Cloud deployments now increasingly provision FPGA accelerators as part of virtual instances. While FPGAs are still essentially single-tenant, the growing demand for hardware acceleration will inevitably lead to the need for methods and architectures supporting FPGA multi-tenancy. In this paper, we propose an architecture supporting space-sharing of FPGA devices among multiple tenants in the cloud. The proposed architecture implements a network-on-chip (NoC) designed for fast data movement and low hardware footprint. Prototyping the proposed architecture on a Xilinx Virtex Ultrascale + 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 (we achieved 6× higher FPGA utilization with our case study), which is one of the major goals of virtualization. Overall, our NoC interconnect achieved about 2× higher maximum frequency than the state-of-the-art and a bandwidth of 25.6 Gbps 

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2. Domain Isolation in FPGA-Accelerated Cloud and Data Center Applications

   https://doi.org/10.1145/3453688.3461527  

   Mandebi Mbongue, Joel; Saha, Sujan Kumar; Bobda, Christophe (June 2021, Proceedings of the 2021 on Great Lakes Symposium on VLSI. 2021)

   null (Ed.)

   Cloud and data center applications increasingly leverage FPGAs because of their performance/watt benefits and flexibility advantages over traditional processing cores such as CPUs and GPUs. As the rising demand for hardware acceleration gradually leads to FPGA multi-tenancy in the cloud, there are rising concerns about the security challenges posed by FPGA virtualization. Exposing space-shared FPGAs to multiple cloud tenants may compromise the confidentiality, integrity, and availability of FPGA-accelerated applications. In this work, we present a hardware/software architecture for domain isolation in FPGA-accelerated clouds and data centers with a focus on software-based attacks aiming at unauthorized access and information leakage. Our proposed architecture implements Mandatory Access Control security policies from software down to the hardware accelerators on FPGA. Our experiments demonstrate that the proposed architecture protects against such attacks with minimal area and communication overhead. 

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3. Accommodating Multi-Tenant FPGAs in the Cloud

   https://doi.org/10.1109/FCCM48280.2020.00045  

   Mbongue, Joel Mandebi; Bobda, Christophe (May 2020, ." 2020 IEEE 28th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM).)

   null (Ed.)

   FPGAs are getting an increasing interest from public clouds and cloud research projects. They are particularly attractive because of their ability to serve as energy efficient and customizable hardware accelerators. Commercial clouds have however highlighted the lack of multi-tenancy support, which does not permit hardware consolidation as it is not possible to space-share FPGA resources between multiple tenants. In this paper, we propose an architecture that divides the FPGA into logically isolated regions that we call ” virtual regions ” (VR). The VRs are immersed in a NoC interconnect allowing flexible communication, fast data movement, and low hardware footprint. The proposed architecture enables multitenancy as VRs can be allocated to different tenants at runtime. 

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4. A Practical Remote Power Attack on Machine Learning Accelerators in Cloud FPGAs

   https://doi.org/10.23919/DATE56975.2023.10136956  

   Tian, Shanquan; Moini, Shayan; Holcomb, Daniel; Tessier, Russell; Szefer, Jakub (April 2023, 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE))

   The security and performance of FPGA-based accelerators play vital roles in today’s cloud services. In addition to supporting convenient access to high-end FPGAs, cloud vendors and third-party developers now provide numerous FPGA accelerators for machine learning models. However, the security of accelerators developed for state-of-the-art Cloud FPGA environments has not been fully explored, since most remote accelerator attacks have been prototyped on local FPGA boards in lab settings, rather than in Cloud FPGA environments. To address existing research gaps, this work analyzes three existing machine learning accelerators developed in Xilinx Vitis to assess the potential threats of power attacks on accelerators in Amazon Web Services (AWS) F1 Cloud FPGA platforms, in a multi-tenant setting. The experiments show that malicious co-tenants in a multi-tenant environment can instantiate voltage sensing circuits as register-transfer level (RTL) kernels within the Vitis design environment to spy on co-tenant modules. A methodology for launching a practical remote power attack on Cloud FPGAs is also presented, which uses an enhanced time-to-digital (TDC) based voltage sensor and auto-triggered mechanism. The TDC is used to capture power signatures, which are then used to identify power consumption spikes and observe activity patterns involving the FPGA shell, DRAM on the FPGA board, or the other co-tenant victim’s accelerators. Voltage change patterns related to shell use and accelerators are then used to create an auto-triggered attack that can automatically detect when to capture voltage traces without the need for a hard-wired synchronization signal between victim and attacker. To address the novel threats presented in this work, this paper also discusses defenses that could be leveraged to secure multi-tenant Cloud FPGAs from power-based attacks. 

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5. 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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