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Because FPGAs outperform traditional processing cores like CPUs and GPUs in terms of performance per watt and flexibility, they are being used more and more in cloud and data center applications. There are growing worries about the security risks posed by multi-tenant sharing as the demand for hardware acceleration increases and gradually gives way to FPGA multi-tenancy in the cloud. The confidentiality, integrity, and availability of FPGA-accelerated applications may be compromised if space-shared FPGAs are made available to many cloud tenants. We propose a root of trust-based trusted execution mechanism called TrustToken to prevent harmful software-level attackers from getting unauthorized access and jeopardizing security. With safe key creation and truly random sources, TrustToken creates a security block that serves as the foundation of trust-based IP security. By offering crucial security characteristics, such as secure, isolated execution and trusted user interaction, TrustToken only permits trustworthy connection between the non-trusted third-party IP and the rest of the SoC environment. The suggested approach does this by connecting the third-party IP interface to the TrustToken Controller and running run-time checks on the correctness of the IP authorization(Token) signals. With an emphasis on software-based assaults targeting unauthorized access and information leakage, we offer a noble hardware/software architecture for trusted execution in FPGA-accelerated clouds and data centers. 

Due to the increasing complexity of modern hetero-geneous System-on-Chips (SoC) and the growing vulnerabilities, security risk assessment and quantification is required to measure the trustworthiness of a SoC. This paper describes a systematic approach to model the security risk of a system for malicious hardware attacks. The proposed method uses graph analysis to assess the impact of an attack and the Common Vulnerability Scoring System (CVSS) is used to quantify the security level of the system. To demonstrate the applicability of the proposed metric, we consider two open source SoC benchmarks with different architectures. The overall risk is calculated using the proposed metric by computing the exploitability and impact of attack on critical components of a SoC. 

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. 

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. 

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. 

Convolutional Neural Networks are compute-intensive learning models that have demonstrated ability and effectiveness in solving complex learning problems. However, developing a high-performance FPGA accelerator for CNN often demands high programming skills, hardware verification, precise distribution localization, and long development cycles. Besides, CNN depth increases by reuse and replication of multiple layers. This paper proposes a programming flow for CNN on FPGA to generate high-performance accelerators by assembling CNN pre-implemented components as a puzzle based on the graph topology. Using pre-implemented components allows us to use the minimum of resources necessary, predict the performance, and gain in productivity since there is no need to synthesize any HDL code. Furthermore, components can be reused for a different range of applications. Through prototyping, we demonstrated the viability and relevance of our approach. Experiments show a productivity improvement of up to 69% compared to a traditional FPGA implementation while achieving over 1.75× higher Fmax with lower resources and power consumption.