Implementation of Elliptic Curve Cryptosystems on a reconfigurable computer

The combination of traditional microprocessors and Field Programmable Gate Arrays (FPGAs) is developing as a future platform for intensive computational computing, combining the best aspects of traditional microprocessor front-end development with the reconfigurability of FPGAs for computation-intensive problems. Several prototype PC-FPGA machines have demonstrated significant speedups compared to standalone PC workstations for computationally intensive problems. Cryptographic applications are a clear candidate for this type of platform, due to their computational intensity and long operand lengths. In this paper, we demonstrate an efficient implementation of IDEA encryption, using two of the leading reconfigurable computers available, SRC Computers’ SRC-6E and Star Bridge Systems’ HC-36. We compare the hardware architecture and programming model of these reconfigurable computers, and the implementation of a common IDEA encryption architecture in both platforms. Detailed analyses of FPGA resource utilization for both systems, data transfer and reconfiguration overheads for the SRC system, and a comparison between SRC and a public domain software implementation are given in the paper.

Current work on automatic task partitioning and scheduling for reconfigurable computing (RC) systems strictly addresses the field programmable gate array (FPGA) hardware, and does not take advantage of the synergy between the microprocessor and the FPGA. Efforts on partitioning between the microprocessor and the FPGA are often times a manual and laborious effort as a formal methodology for automatic hardware-software partitioning for RC systems has not yet been established. Related fields such as heterogeneous computing (HC) and embedded computing (EC) have an extensive body of work for scheduling for heterogeneous processors. In this work, we adapt HC scheduling algorithms for RC systems, and show how simply adapting the algorithms alone is not sufficient to take advantage of the reconfigurable hardware. In many cases, the HC heuristics algorithms do not generate efficient schedules necessary to take advantage of the synergy between the microprocessor and the FPGA. We introduce new heuristic algorithms based on HC scheduling algorithms and show that they provide up to an order of magnitude improvement in execution time.

Reconfigurable computing provides a paradigm to create intelligent systems different from the classic software computing approach. Instead of using a processor with an instruction set, a full stack of middleware, and an application program running on top, the field-programmable gate arrays (FPGAs) integrate a cell set that can be configured in different ways. A few vendors have dominated this market with their proprietary tools, hardware devices, and boards, resulting in fragmented ecosystems with few standards and little interoperation. However, a new and complete toolchain for FPGAs with its associated open tools has recently emerged from the open-source community. Robotics is an expanding application field that may definitely benefit from this revolution, as fast speed and low power consumption are usual requirements. This paper hypothesizes that basic reactive robot behaviors may be easily designed following the reconfigurable computing approach and the state-of-the-art open FPGA toolchain. They provide new abstractions such as circuit blocks and wires for building intelligent robots. Visual programming and block libraries make such development painless and reliable. As experimental validation, two reactive behaviors have been created in a real robot involving common sensors, actuators, and in-between logic. They have been also implemented using classic software programming for comparison purposes. Results are discussed and show that the development of reactive robot behaviors using reconfigurable computing and open tools is feasible, also achieving a high degree of simplicity and reusability, and benefiting from FPGAs’ low power consumption and time-critical responsiveness.

The TCFPGA Hall of Fame for FPGAs (field-programmable gate arrays) and Reconfigurable Computing recognizes the most significant peer-reviewed publications in the field, highlights key contributions, and represents the body of knowledge that has accumulated over the past 30 years. The ACM SIGDA Technical Committee on FPGAs and Reconfigurable Computing is a technical committee of the Design Automation Special Interest Group, which was formed to promote the FPGA and reconfigurable computing community.

The reconfigurable computing (RC) aims to combine the flexibility of General-Purpose Processor (GPP) with performance of Application Specific Integrated Circuits (ASIC). There are several architectures proposed since RC’s inception in 1960s, but all have failed to become mainstream. The main factor preventing RC to become common practice is its requirement for implementers of algorithms (programmers) to be familiar with hardware design. In RC, a hardened processor cooperates with a dynamic reconfigurable Hardware Accelerator (HA) which is implemented on Field-Programmable Gate Array (FPGA). The HA implements crucial software kernel on hardware to increase performance and its design demands digital circuit expertise. In this paper a novel RC architecture is proposed that keeps the decades old programming practices intact while harnessing the power of HA. The architecture uses LLVM compiler infrastructure to receive an algorithm and then outputs the equivalent machine language, it then finds the most frequent instruction pairs and generates equivalent RC circuit called “Miniature Accelerator (MA)”. The instruction pairs are dynamically removed from pipeline and MA computed result replaces them in parallel. To demonstrate the concept the Fast Fourier Transform (FFT) algorithm which is core Digital signal processing (DSP) kernel is written in C and then executed on an ARM Cortex-M0. The execution of FFT function is improved by 14.12%. The proposed adaptive processor is fully backward compatible, compilation is automated, and no modification of exiting software or established programming paradigms is required.

The emerging reconfigurable computing reduces the need of computation exhaustive applications, which are always demanding more efficient computation hardware. The partially reconfigurable Field Programmable Gate Arrays (FPGA) are highly suitable for performance improvement. This paper discusses the study of FPGA utilization when scheduling fixed size configurable computation hardware block (CCHB) by applying a heuristic. Based on the parameters (speedup, CCHB size etc.,) associated with the independent CCHBs, scheduling is performed and it is repeated for various sizes of FPGA. From the study of four applications from benchmark suite, it is observed that the device utilization is increased with size of CCHBs not greater than 0.5 times or not less than 0.85 times of the size of FPGA.

This paper describes the stochastic-based Spin-Programmable Gate Array (SPGA), an innovative architecture attempting to exploit the stochastic switching behavior newly found in emerging spintronic devices for reconfigurable computing. While many recently studies have investigated using Spin Transfer Torque Memory (STTM) devices to replace configuration memory in field programmable gate arrays (FPGAs), our study, for the first time, attempts to use the quantum-induced stochastic property exhibited by spintronic devices directly for reconfiguration and logic computation. Specifically, the SPGA was designed from scratch for high performance, routability, and ease-of-use. It supports variable-granularity multiple-input-multiple-output (MIMO) logic blocks and variable-length bypassing interconnects with a symmetrical structure. Due to its unconventional architectural features, the SPGA requires several major modifications to be made in the standard VPR placement/routing CAD flow, which include a new technology mapping algorithm based on computing (k, l)-cut, a new placement algorithm, and a modified delay-based routing procedure.Previous studies have shown that, simply replacing reconfiguration memory bits with spintronic devices, the conventional 2D island-style FPGA architecture can achieve approximately 5 times area savings, 2 times speedup and 1.6 times power savings. Our mixed-mode simulation results have shown that, with FPGA architecture innovations, on average, a SPGA can further achieve more than 10 times improvement in logic density, about 5 times improvement in average net delay, and about 5 times improvement in the critical-path delay for the largest 12 MCNC benchmark circuits over an island-style baseline FPGA with spintronic configuration bits.

The paper covers the reconfigurable computer systems based on high integration field programmable gate arrays (FPGAs) of Xilinx Virtex UltraScale+ series, where immersion liquid cooling system is used for cooling of all electronic components. The original engineering and technological solutions for thermal interface, cooling liquid, radiators and the entire reconfigurable computer system are considered. Owing to these features it is possible to achieve unprecedented layout density, up to 128 FPGAs for a 3U computational module placed into a standard \(19^{{\prime }{\prime }}\) computer rack. The paper presents results of prototyping and experimental verification of energy-efficient computational module with immersion liquid cooling system. On the base of the new computational module it is possible to achieve the performance of 1 Pflops in a standard 47U rack with total power consumption not more than 150 kW. The designed immersion liquid cooling system has power reserve for already produced and for next-generation high integration FPGA series, resistance to leaks and their consequences, and compatibility with traditional water cooling systems based on industrial chillers.

In the presented work the FPGA based run-time reconfigurable platform with temporal partitioning of hardware resources is proposed. This platform is based on the Field Programmable Gate Array (FPGA) device that can be reconfigured "on-fly" to provide the optimal adaptation of a processing architecture to the algorithm and data structure by utilization of developed mechanisms of temporal partitioning of computational / logic resources. It was shown that the proposed approach allows reaching very high cost-effectiveness of the computing platform oriented on processing of framed data-streams. On the other hand, the hardware programming and compilation processes could be simplified by utilization of library of precompiled Virtual Hardware Components stored in the on-board FLASH memory. Paper presents theoretical proof of the proposed approach by analytical comparison of the performance that could be reached on the conventional processors and FPGA platform with Temporal Partitioning Mechanism (TPM) of hardware resources. The implementation of the proposed TPM on the basis of Xilinx Spartan-3 and Xilinx Virtex II FPGA devices is described. Experimental results gained on the prototype of the FPGA based platform with TPM are discussed and analyzed. Keywords: reconfigurable computing, data-stream processing, FPGA, run-time reconfiguration, temporal partitioning.

Building the machines that advance scientific knowledge is one of the most significant challenges of our time. So-called Big Science instruments are unique; the expertise required to build them is multidisciplinary, and few countries can build and maintain them. Since resources and budgets are not unlimited, these require efficient solutions. Big Science machines take a decade to plan, a decade to build, and at least a decade to operate. One such machine is the largest fusion reactor project, ITER, which promises to be the next step in fusion research and has a big challenge in system integration. To mitigate the integration effort, ITER has invested in standardization, both in software and hardware, introducing standard instrumentation software named Nominal Device Support (NDSv3); Micro Telecommunication Computing Architecture (MTCA) as the long-term hardware standard choice, and EPICS as a general-purpose distributed control system. These control systems are essential in the management of the facility. They provide functions such as communication protocols, process management, monitoring, alarms, and human-machine interfaces. But also, more advanced access to databases or data acquisition systems. The latter is typically managed by a Field Programmable Gate Array (FPGA), of which facilities can easily have thousands of these FPGAs managing millions of signals within the control system environment. Nevertheless, these FPGA data acquisition (DAQ) systems continue to be labor-intensive. Standardized, modular, and open-source frameworks have been proposed to solve this problem, however, the FPGA programming expertise requirement remained. This thesis proposes a complete technology stack that considers these DAQ systems from the control system to the hardware. It provides a methodology using the Open Computing Language (OpenCL) to reduce the FPGA programming effort. The FPGA devices are programmed using C/C++-like environments, which are much simpler than traditional Hardware Description Languages (HDLs). The software, named IRIO-OpenCL, is written in C++ following ITER's NDSv3 model, which abstracts and generalizes the development of software device drivers and simplifies the interface to EPICS. IRIO-OpenCL is specifically designed to simplify the integration of intelligent instrumentation systems, allowing the integration of algorithms into the FPGA without modifying large parts of the supporting library. The MTCA standard was chosen to maintain modularity. The chassis backplane connects to the devices via Ethernet and PCIe, enabling standard communications. The Advanced Mezzanine Card is the typical MTCA FPGA-based DAQ carrier board. It connects the FPGA to the chassis backplane and typically provides FPGA Mezzanine Connectors (FMCs) for plugging in analog-to-digital (ADC) carrier boards. An ADC connected via the FMC interface samples the analog signals passed to the OpenCL processing kernels. Using OpenCL, the FPGA kernels communicate with the host machine in a standardized way. To demonstrate the applicability of this methodology, reference problems were selected from the literature and implemented, both with traditional and deep learning (DL) solutions. The methodology elaborates on connecting the OpenCL kernels for any use case, and the results show examples of kernel sets. The neutron flux measurement problem is solved at 1GS/s in real time using pulse counting and Campbell techniques, using 40% of the target Arria10 FPGA. The DL application example is based on a neutron/gamma discrimination neural network (NN) that achieves a classification rate of up to 1.3 MEvents/s using less than 25% of the target resource. RESUMEN Construir máquinas que avancen el conocimiento científico es uno de los desafíos más significativos de nuestro tiempo. Los instrumentos de la llamada Big Science son únicos; las competencias requeridas para su construcción son multidisciplinares, y pocos países pueden construirlos y mantenerlos. Dado que los recursos y presupuestos no son ilimitados, estos requieren soluciones eficientes. Las máquinas de Gran Ciencia tardan una década en planificarse, una década en construirse y al menos una década en operarse. Una de estas máquinas es el proyecto del reactor de fusión más grande, ITER, que promete ser el siguiente paso en la investigación de fusión y presenta un gran desafío en la integración de sistemas. Para mitigar el esfuerzo de integración, ITER ha invertido en estandarización, tanto en software como en hardware, introduciendo un software de instrumentación estándar llamado Nominal Device Support v3 (NDSv3); Micro Telecommunication Computing Architecture (MTCA) como la elección estándar de hardware a largo plazo, y EPICS como un sistema de control distribuido de propósito general. Estos sistemas de control son esenciales en la gestión de la instalación ya que proporcionan funciones como protocolos de comunicación, gestión de procesos, monitorización, alarmas e interfaces de usuario, además de acceso avanzado a bases de datos o sistemas de adquisición de datos. Este último es típicamente gestionado por un Field Programmable Gate Array (FPGA), de los cuales las instalaciones pueden tener fácilmente miles de unidades gestionando millones de señales dentro del entorno del sistema de control. Estos sistemas de adquisición de datos (DAQ) con FPGA siguen requiriendo programación experta y laboriosa. Se han propuesto marcos estandarizados, modulares y de código abierto para resolver este problema, sin embargo, la necesidad de experiencia en programación FPGA no se ha podido eludir. Esta tesis propone una pila de tecnologías que considera estos sistemas DAQ desde el sistema de control hasta el hardware. Proporciona una metodología utilizando el Open Computing Language (OpenCL) para reducir el esfuerzo de programación FPGA. Los dispositivos FPGA se programan utilizando entornos similares a C/C++, que son mucho más simples que los Lenguajes de Descripción de Hardware (HDL) tradicionales. El software, llamado IRIO-OpenCL, está escrito en C++ siguiendo el modelo de NDSv3 de ITER, que abstrae y generaliza el desarrollo de controladores de dispositivos de software y simplifica la interfaz con EPICS. IRIO-OpenCL está específicamente diseñado para simplificar la integración de sistemas de instrumentación inteligentes, permitiendo la integración de algoritmos en el FPGA sin modificar grandes partes de la biblioteca de soporte. El estándar MTCA fue elegido para mantener la modularidad. El backplane del chasis se conecta a los dispositivos a través de Ethernet y PCIe, habilitando comunicaciones estándar. La Advanced Mezzanine Card es la placa portadora DAQ FPGA típica de MTCA, que conecta el FPGA al backplane del chasis y típicamente proporciona FPGA Mezzanine Connectors (FMC) para conectar placas con conversores analógico-digitales (ADC). Un ADC conectado a través de la interfaz FMC muestrea las señales analógicas pasadas a los núcleos de procesamiento OpenCL. Utilizando OpenCL, los núcleos FPGA se comunican con la máquina anfitriona de una manera estandarizada. Para demostrar la aplicabilidad de esta metodología, se seleccionaron e implementaron problemas de referencia de la literatura, tanto con soluciones tradicionales como de Deep Learning (DL). La metodología elabora ejemplos de conexión de núcleos OpenCL para cualquier caso de uso, y los resultados muestran ejemplos de conjuntos de núcleos. El problema de medición del flujo de neutrones se resuelve a 1GS/s en tiempo real utilizando técnicas de conteo de pulsos y Campbell, utilizando el 40% del objetivo FPGA Arria10. El ejemplo de aplicación DL se basa en una red neuronal para la discriminación neutrones/gammas que logra una tasa de clasificación de hasta 1.3 MEventos/s utilizando menos del 25% de la FPGA objetivo. LABURPENA Ezagutza zientifikoa aurreratzen duten makinak eraikitzea gure garaiko erronka garrantzitsuenetako bat da. Big Science deritzonaren makinak bakarrak dira; beharrezko esperientzia diziplina anitzekoa da, eta herrialde gutxik eraiki eta mantendu ditzakete. Baliabideak eta aurrekontuak mugagabeak ez direnez, konponbide eraginkorrak behar dira. Zientzia Handi makinak hamarkada bat behar dute diseinatuak izateko, hamarkada bat eraikitzeko eta gutxienez hamarkada bat jarduteko. Makina horietako bat fusio-erreaktore handienaren proiektua da, ITER, fusio-ikerketan hurrengo urratsa izango dena eta sistemak integratzeko erronka handia aurkezten duena. Integrazio-ahalegina arintzeko, ITERek estandarizazioan inbertitu du, bai softwarean bai - eta EPICS helburu orokorreko kontrol-sistema banatua. Kontrol-sistema horiek funtsezkoak dira instalazioa kudeatzeko. Komunikazio-protokoloak, prozesuen kudeaketa, monitorizazioa, alarmak eta pertsona-makina interfazeak bezalako funtzioak ematen dituzte, datubaseetarako edo datuak eskuratzeko sistemetarako sarbide aurreratua izateaz gain. Azken hori "Field Programmable Gate Array" (FPGA) batek kudeatzen du, milaka FPGA konektatuz makina bakoitzean eta kontrol-sistemaz milioika seinale kudeatuz. FPGA duten datuak eskuratzeko sistemek (DAQ) programazio aditua eta neketsua eskatzen jarraitzen dute. Problema hori ebazteko marko estandarizatuak, modularrak eta kode irekikoak proposatu dira, hala ere, FPGA hardware programazio espezializatuaren beharra ezin izan da ukatu. Tesi honek DAQ sistema hauek kontrol sistematik hardwareraino kontsideratuz teknologia bilduma bat programazio-ahalegina murrizten du. FPGA gailuak C/C++ antzeko inguruneak erabiliz programatzen dira, - OpenCL izenekoa, C++n idatzita dago, ITERren "Nominal Device Support v3" (NDSv3) eredua jarraituz. Honek software-gailuen garapena diseinu abstraktu eta orokorrez EPICSekiko interfazea sinplifikatzen du. IRIOOpenCL berariaz diseinatuta dago instrumentazio-sistema adimendunen integrazioa sinplifikatzeko, eta algoritmoak FPGAn integratzea ahalbidetzen du, software zati handiak aldatu gabe. Modularitateari eusteko, MTCA estandarra aukeratu da. Txasisaren backplanea Ethernet eta PCIe bidez konektatzen da gailuetara, komunikazio estandarrak gaituz. "Advanced Mezzanine Card" MTCAren ohiko DAQ FPGA plaka eramailea da. FPGA txasisaren backplanera konektatzen du eta, normalean, "FPGA Mezzanine Connectors"ak (FMC) ditu plaka bihurgailu analogiko-digitalak (ADC) konektatzeko. ADCak, FMC interfazearen bidez konektatuta, OpenCL prozesatze-nukleoetara igarotako seinale analogikoak bihurtzen ditu. OpenCL erabiliz, FPGA nukleoak ordenagailuarekin modu estandarizatuan komunikatzen dira. Metodologia horren aplikagarritasuna egiaztatzeko, erreferentziazko adibideak literaturatik hautatu eta nukleoen konexio-adibideak egiten ditu, edozein kasutan erabiltzeko, eta emaitzek nukleo multzoen adibideak erakusten dituzte. Neutroi-fluxuaren neurketak 1GS/s-ko ebazten dira real time errendimenduaz, pultsuak eta Campbell zenbatzeko teknikak erabiliz, Arria10 FPGAren % 40 erabiliz. Azkenik, DL aplikazioaren adibidea, neutroi/gamma sailkapena, sare neuronal batean oinarritzen da, eta FPGAren baliabideen % 25 baino gutxiago erabiliz 1.3 MSailkapen/s sailkapen-tasa lortzen du.

Reconfigurable computers are a new class of customisable computers constructed using field programmable gate arrays (FPGAs). They offer us the potential for supercomputing performance at high-end workstation costs for a range of niche applications. The performance achievable is a direct consequence of the machine architecture which gives the user direct exposure to the inherent parallelism present in the underlying FPGA circuits, in contrast to classical, sequential, von Neumann architectures. FPGAs are reprogrammable so giving reconfigurable computers the unique combination of software-like reprogrammability with custom hardware performance.

This paper presents new results on an innovative approach for solving satisfiability problems (SAT), that is, creating a logic circuit that is specialized to solve each problem instance on Field Programmable Gate Arrays (FPGAs). This approach has become feasible due to recent advances in Reconfigurable Computing, and has opened up an exciting new research field in algorithm design.We have developed an algorithm that is suitable for a logic circuit implementation. This algorithm is basically equivalent to the Davis-Putnam procedure with Experimental Unit Propagation. The algorithm requires fewer hardware resources than previous approaches. Simulation results show that this method can solve a hard random 3-SAT problem with 400 variables within 1.6 minutes at a clock rate of 10MHz. Faster speeds can be obtained by increasing the clock rate. Furthermore, we have actually implemented a 128-variable, 256-clause problem instance on FPGAs.

The demand for high-performance computing and rapidly increasing power consumption has increased the necessity for application-specific accelerators. In the datacenter and mobile system, more applications are increasingly relying on accelerators. Field-programmable gate arrays (FPGAs) emerge as a good candidate because they have high programmability and power efficiency. As the number of applications requiring acceleration increases, there is huge demand for FPGAs that support multiple contexts. Previous FPGA designs that support multicontext have various shortcomings such as volatility, poor power efficiency, large performance, area, and reconfiguration overhead. In this article, we propose a spin-transfer torque magnetic RAM (STT-MRAM)-based nonvolatile multicontext FPGA (NVMC-FPGA) that overcomes these shortcomings. We introduce the NVMC-FPGA architecture and operation modes that take advantage of nonvolatility and support multicontext. We also develop the multicontext-aware FPGA computer aided design flow to make the most of the NVMC-FPGA. Compared to the conventional SRAM-based FPGA, when eight identical circuits are mapped, the NVMC-FPGA improves the performance by 15.3% on average and reduces the power consumption by 11.2%–80.7%, depending on the number of simultaneously activated circuits. Moreover, when eight different circuits are mapped, the NVMC-FPGA improves the performance by 58.5% on average and reduces the power consumption by 6.2%–63.3%, depending on the number of simultaneously activated circuits.

The authors explore and design the traditional field-programmable gate array (FPGA) interconnect topologies and architectures that can play an important role in improving performance and density. The main architectures under exploration are tree-based and island-style Manhattan Mesh. Mesh-based design is the most common industrial and academic architecture. Numerous research and industrial designs have been developed to improve mesh-based FPGAs' performance, area, and power consumption. Nonetheless, when looking at performance metrics such as speed, area, and power, the gap is generally very wide for FPGAs compared to application-specific integrated circuits (ASICs). Despite their good properties, such as high logic density and area advantage, tree-based architectures have been overlooked up to now. This article addresses long wire length issues associated with tree-based programmable interconnects using 3D design and manufacturing technologies. Using their dedicated 3D design and optimization methodology, the authors show that 3D tree-based architecture is 1.5 times faster than the mesh-based counterpart.

Since their introduction in 1985, Field Programmable Gate Array (FPGA) devices have been used in tens of thousands of designs. In most of these, logic implemented in an FPGA could have been implemented with more traditional logic devices-masked gate arrays, or MSI/SSI devices, for example. However, FPGAs were a better alternative for any number of reasons-no nonrecurring engineering costs, lower design risks, less power dissipation, faster design and production cycles, etc. In essence, FPGAs combine the high-integration benefits of gate arrays with the time-to-market benefits of a user-programmable device, and typically are selected over other logic technologies to gain these benefits. >