Reconfigurable computing refers to the use of processors, such as Field Programmable Gate Arrays (FPGAs), that can be modified at the hardware level to take on different processing tasks. A reconfigurable computing platform describes the hardware and software base on top of which modular extensions can be created, depending on the desired application. Such reconfigurable computing platforms can take on varied designs and implementations, according to the constraints imposed and features desired by the scope of applications. This paper introduces a PC-based reconfigurable computing platform software frameworks that is flexible and extensible enough to abstract the different hardware types and functionality that different PCs may have. The requirements of the software platform, architectural issues addressed, rationale behind the decisions made, and frameworks design implemented are discussed.
Many reconfigurable platforms require that applications be written specifically to take advantage of the reconfigurable hardware. In a PC-based environment, this presents an undesirable constraint in that the many already available applications cannot leverage on such hardware. Greatest benefit can only be derived from reconfigurable devices if even native OS applications can transparently utilize reconfigurable devices as they would normal full-fledged hardware devices. This paper presents how Proteus Virtual Devices are used to expose reconfigurable hardware in a transparent manner for use b…
Modern embedded and cyber-physical systems require every day more performance, power efficiency and flexibility, to execute several profiles and functionalities targeting the ever growing adaptivity needs and preserving execution efficiency. Such requirements pushed designers towards the adoption of heterogeneous and reconfigurable substrates, which development and management is not that straightforward. Despite acceleration and flexibility are desirable in many domains, the barrier of hardware deployment and operation is still there since specific advanced expertise and skills are needed. Rel…
The exponential emergence of Field Programmable Gate Array (FPGA) has accelerated the research of hardware implementation of Deep Neural Network (DNN). Among all DNN processors, domain specific architectures, such as, Google's Tensor Processor Unit (TPU) have outperformed conventional GPUs. However, implementation of TPUs in reconfigurable hardware should emphasize energy savings to serve the green computing requirement. Voltage scaling, a popular approach towards energy savings, can be a bit critical in FPGA as it may cause timing failure if not done in an appropriate way. In this work, we pr…
In the last decade we have witnessed a rapid growth in data center systems, requiring new and highly complex networking devices. The need to refresh networking infrastructure whenever new protocols or functions are introduced, and the increasing costs that this entails, are of a concern to all data center providers. New generations of Systems on Chip (SoC), integrating microprocessors and higher bandwidth interfaces, are an emerging solution to this problem. These devices permit entirely new systems and architectures that can obviate the replacement of existing networking devices while enablin…
In this chapter, we study the feasibility of implementing SOR in reconfigurable hardware. We use Handel-C, a higher level design tool, to code our design, which is analyzed, synthesized, and placed and routed using the FPGAs proprietary software (DK Design Suite, Xilinx ISE 8.1i, and Quartus II 5.1). We target Virtex II Pro, Altera Stratix, and Spartan3L, which is embedded in the RC10 FPGA-based system from Celoxica. We report our timing results when targeting Virtex II Pro and compare them to software version results written in C++ and running on a general purpose processor (GPP).
The growing sophistication of cyber attacks, vulnerabilities in high computing systems and increasing dependency on cryptography to protect our digital data make it more important to keep secret keys safe and secure. Few major issues on secret keys like incorrect use of keys, inappropriate storage of keys, inadequate protection of keys, insecure movement of keys, lack of audit logging, insider threats and non-destruction of keys can compromise the whole security system dangerously. In this article, we have proposed and implemented an isolated secret key memory which can log life cycle of secre…
Coarse-Grained Reconfigurable Arrays (CGRAs) enable ease of programmability and result in low development costs. They enable the ease of use specifically in reconfigurable computing applications. The smaller cost of compilation and reduced reconfiguration overhead enables them to become attractive platforms for accelerating high-performance computing applications such as image processing. The CGRAs are ASICs and therefore, expensive to produce. However, Field Programmable Gate Arrays (FPGAs) are relatively cheaper for low volume products but they are not so easily programmable. We combine best…
The use of reconfigurable computing, and FPGAs in particular, has strong potential in the field of High Performance Computing (HPC). However the traditionally high barrier to entry when it comes to programming this technology has, until now, precluded widespread adoption. To popularise reconfigurable computing with communities such as HPC, Xilinx have recently released the first version of Vitis, a platform aimed at making the programming of FPGAs much more a question of software development rather than hardware design. However a key question is how well this technology fulfils the aim, and wh…
The ongoing shift of cloud services from monolithic designs to microservices creates high demand for efficient and high performance datacenter networking stacks, optimized for fine-grained workloads. Commodity networking systems based on software stacks and peripheral NICs introduce high overheads when it comes to delivering small messages. We present Dagger, a hardware acceleration fabric for cloud RPCs based on FPGAs, where the accelerator is closely-coupled with the host processor over a configurable memory interconnect. The three key design principle of Dagger are: (1) offloading the entir…
hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware
Purdue University（普渡大学）；ETH Zurich（苏黎世联邦理工学院）；California Institute of Technology（加州理工学院）；Fermi National Accelerator Lab（费米国家加速器实验室）；European Organization for Nuclear Research (CERN)（欧洲核子研究中心）；University of California San Diego（加州大学圣地亚哥分校）；Imperial College London（伦敦帝国理工学院）；National Technical University of Athens（希腊国家技术大学）；University of Geneva（日内瓦大学）；Altera Corporation（阿尔特拉公司）；Discovery Partners Institute（发现伙伴研究所）；Massachusetts Institute of Technology（麻省理工学院）；National Yang Ming Chiao Tung University（国立阳明交通大学）；Northwestern University（西北大学）；Princeton University（普林斯顿大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Michigan（密歇根大学）；University of Pennsylvania（宾夕法尼亚大学）；University of Washington（华盛顿大学）
For decades, advances in electronics were directly driven by the scaling of CMOS transistors according to Moore's law. However, both the CMOS scaling and the classical computer architecture are approaching fundamental and practical limits, and new computing architectures based on emerging devices, such as resistive random-access memory (RRAM) devices, are expected to sustain the exponential growth of computing capability. Here we propose a novel memory-centric, reconfigurable, general purpose computing platform that is capable of handling the explosive amount of data in a fast and energy-effic…
FPGAs have found their way into data centers as accelerator cards, making reconfigurable computing more accessible for high-performance applications. At the same time, new high-level synthesis compilers like Xilinx Vitis and runtime libraries such as XRT attract software programmers into the reconfigurable domain. While software programmers are familiar with task-level and data-parallel programming, FPGAs often require different types of parallelism. For example, data-driven parallelism is mandatory to obtain satisfactory hardware designs for pipelined dataflow architectures. However, software…
