Constructing cluster of simple FPGA boards for cryptologic computations

In this paper, we propose an FPGA cluster infrastructure, which can be utilized in implementing cryptanalytic attacks and accelerating cryptographic operations. The cluster can be formed using simple and inexpensive, off-the-shelf FPGA boards featuring an FPGA device, local storage, CPLD, and network connection. Forming the cluster is simple and no effort for the hardware development is needed except for the hardware design for the actual computation. Using a softcore processor on FPGA, we are able to configure FPGA devices dynamically and change their configuration on the fly from a remote computer. The softcore on FPGA can execute relatively complicated programs for mundane tasks unworthy of FPGA resources. Finally, we propose and implement a fast and efficient dynamic configuration switch technique that is shown to be useful especially in cryptanalytic applications. Our infrastructure provides a cost-effective alternative for formerly proposed cryptanalytic engines based on FPGA devices

Scalability of spin FPGA: A Reconfigurable Architecture based on spin MOSFET

Scalability of Field Programmable Gate Array (FPGA) using spin MOSFET (spin FPGA) with magnetocurrent (MC) ratio in the range of 100% to 1000% is discussed for the first time. Area and speed of million-gate spin FPGA are numerically benchmarked with CMOS FPGA for 22nm, 32nm and 45nm technologies including 20% transistor size variation. We show that area is reduced and speed is increased in spin FPGA owing to the nonvolatile memory function of spin MOSFET.Comment: 3 pages, 7 figure

Dynamic reconfiguration technologies based on FPGA in software defined radio system

Partial Reconfiguration (PR) is a method for Field Programmable Gate Array (FPGA) designs which allows multiple applications to time-share a portion of an FPGA while the rest of the device continues to operate unaffected. Using this strategy, the physical layer processing architecture in Software Defined Radio (SDR) systems can benefit from reduced complexity and increased design flexibility, as different waveform applications can be grouped into one part of a single FPGA. Waveform switching often means not only changing functionality, but also changing the FPGA clock frequency. However, that is beyond the current functionality of PR processes as the clock components (such as Digital Clock Managers (DCMs)) are excluded from the process of partial reconfiguration. In this paper, we present a novel architecture that combines another reconfigurable technology, Dynamic Reconfigurable Port (DRP), with PR based on a single FPGA in order to dynamically change both functionality and also the clock frequency. The architecture is demonstrated to reduce hardware utilization significantly compared with standard, static FPGA design

Application Acceleration on FPGAs with OmpSs@FPGA

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.OmpSs@FPGA is the flavor of OmpSs that allows offloading application functionality to FPGAs. Similarly to OpenMP, it is based on compiler directives. While the OpenMP specification also includes support for heterogeneous execution, we use OmpSs and OmpSs@FPGA as prototype implementation to develop new ideas for OpenMP. OmpSs@FPGA implements the tasking model with runtime support to automatically exploit all SMP and FPGA resources available in the execution platform. In this paper, we present the OmpSs@FPGA ecosystem, based on the Mercurium compiler and the Nanos++ runtime system. We show how the applications are transformed to run on the SMP cores and the FPGA. The application kernels defined as tasks to be accelerated, using the OmpSs directives are: 1) transformed by the compiler into kernels connected with the proper synchronization and communication ports, 2) extracted to intermediate files, 3) compiled through the FPGA vendor HLS tool, and 4) used to configure the FPGA. Our Nanos++ runtime system schedules the application tasks on the platform, being able to use the SMP cores and the FPGA accelerators at the same time. We present the evaluation of the OmpSs@FPGA environment with the Matrix Multiplication, Cholesky and N-Body benchmarks, showing the internal details of the execution, and the performance obtained on a Zynq Ultrascale+ MPSoC (up to 128x). The source code uses OmpSs@FPGA annotations and different Vivado HLS optimization directives are applied for acceleration.This work is partially supported by the European Union H2020 program through the EuroEXA project (grant 754337), and HiPEAC (GA 687698), by the Spanish Government through Programa Severo Ochoa (SEV-2015- 0493), by the Spanish Ministry of Science and Technology (TIN2015-65316-P) and the Departament d’Innovació Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programació i Entorns d’Execució Paral·lels (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

FPGA-Based Bandwidth Selection for Kernel Density Estimation Using High Level Synthesis Approach

FPGA technology can offer significantly hi\-gher performance at much lower power consumption than is available from CPUs and GPUs in many computational problems. Unfortunately, programming for FPGA (using ha\-rdware description languages, HDL) is a difficult and not-trivial task and is not intuitive for C/C++/Java programmers. To bring the gap between programming effectiveness and difficulty the High Level Synthesis (HLS) approach is promoting by main FPGA vendors. Nowadays, time-intensive calculations are mainly performed on GPU/CPU architectures, but can also be successfully performed using HLS approach. In the paper we implement a bandwidth selection algorithm for kernel density estimation (KDE) using HLS and show techniques which were used to optimize the final FPGA implementation. We are also going to show that FPGA speedups, comparing to highly optimized CPU and GPU implementations, are quite substantial. Moreover, power consumption for FPGA devices is usually much less than typical power consumption of the present CPUs and GPUs.Comment: 23 pages, 6 figures, extended version of initial pape

Development of an FPGA-based gate signal generator for a multilevel inverter

The application of Field Programmable Gate Array (FPGA) in the development of power electronics circuits control scheme has drawn much attention lately due to its shorter design cycle, lower cost and higher density. This paper presents an FPGA-based gate signal generator for a multilevel inverter employing an online optimal PWM switching strategy to control its output voltage. FPGA is chosen for the hardware implementation of the switching strategy mainly due to its high computation speed that can ensure the accuracy of the instants that gating signals are generated. The gate signal generator has been realized by an FPGA (FLEXlOKZO) from Altera. The design and development of the FPGA based gate signal generator is described in detail. Results from the timing simulation using MAX+PLUSII software are given and verified by the results obtained from the FLEXlOK2O output

Supporting task creation inside FPGA devices

The most common model to use co-processors/accelerators is the master-slave model where the slaves (coprocessors/ accelerators) are driven by a general purpose cpu. This simplifies the management of the accelerators because they cannot actively interact with the runtime and they are just passive slaves that operate over the memory under demand. However, the master-slave model limits system possibilities and introduces synchronization overheads that could be avoided. To overcome those limitations and increase the possibilities of accelerators, we propose extending task based programming models (like OpenMP [1] or OmpSs) to support some runtime APIs inside the FPGA co-processor. As a proof-of-concept, we implemented our proposal over the OmpSs@FPGA environment [2] adding the needed infrastructure in the FPGA bitstream and modifying the existing tools to support creation of children tasks inside a task offloaded to an FPGA accelerator. In addition, we added support to synchronize the children tasks created by a FPGA task regardless they are executed in a SMP host thread or they also target another FPGA accelerator in the same co-processor