A Study of Reconfigurable Accelerators for Cloud Computing

Due to the exponential increase in network traffic in the data centers, thousands of servers interconnected with high bandwidth switches are required. Field Programmable Gate Arrays (FPGAs) with Cloud ecosystem offer high performance in efficiency and energy, making them active resources, easy to program and reconfigure. This paper looks at FPGAs as reconfigurable accelerators for the cloud computing presents the main hardware accelerators that have been presented in various widely used cloud computing applications such as: MapReduce, Spark, Memcached, Databases

Quantifying the latency benefits of near-edge and in-network FPGA acceleration

Transmitting data to cloud datacenters in distributed IoT applications introduces significant communication latency, but is often the only feasible solution when source nodes are computationally limited. To address latency concerns, cloudlets, in-network computing, and more capable edge nodes are all being explored as a way of moving processing capability towards the edge of the network. Hardware acceleration using Field Programmable Gate Arrays (FPGAs) is also seeing increased interest due to reduced computation latency and improved efficiency. This paper evaluates the the implications of these offloading approaches using a case study neural network based image classification application, quantifying both the computation and communication latency resulting from different platform choices. We consider communication latency including the ingestion of packets for processing on the target platform, showing that this varies significantly with the choice of platform. We demonstrate that emerging in-network accelerator approaches offer much improved and predictable performance as well as better scaling to support multiple data sources

Multi-Tenant Cloud FPGA: A Survey on Security

With the exponentially increasing demand for performance and scalability in cloud applications and systems, data center architectures evolved to integrate heterogeneous computing fabrics that leverage CPUs, GPUs, and FPGAs. FPGAs differ from traditional processing platforms such as CPUs and GPUs in that they are reconfigurable at run-time, providing increased and customized performance, flexibility, and acceleration. FPGAs can perform large-scale search optimization, acceleration, and signal processing tasks compared with power, latency, and processing speed. Many public cloud provider giants, including Amazon, Huawei, Microsoft, Alibaba, etc., have already started integrating FPGA-based cloud acceleration services. While FPGAs in cloud applications enable customized acceleration with low power consumption, it also incurs new security challenges that still need to be reviewed. Allowing cloud users to reconfigure the hardware design after deployment could open the backdoors for malicious attackers, potentially putting the cloud platform at risk. Considering security risks, public cloud providers still don't offer multi-tenant FPGA services. This paper analyzes the security concerns of multi-tenant cloud FPGAs, gives a thorough description of the security problems associated with them, and discusses upcoming future challenges in this field of study