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

OPTIMAL AREA AND PERFORMANCE MAPPING OF K-LUT BASED FPGAS

FPGA circuits are increasingly used in many fields: for rapid prototyping of new products (including fast ASIC implementation), for logic emulation, for producing a small number of a device, or if a device should be reconfigurable in use (reconfigurable computing). Determining if an arbitrary, given wide, function can be implemented by a programmable logic block, unfortunately, it is generally, a very difficult problem. This problem is called the Boolean matching problem. This paper introduces a new implemented algorithm able to map, both for area and performance, combinational networks using k-LUT based FPGAs.k-LUT based FPGAs, combinational circuits, performance-driven mapping.

A general framework for efficient FPGA implementation of matrix product

Original article can be found at: http://www.medjcn.com/ Copyright Softmotor LimitedHigh performance systems are required by the developers for fast processing of computationally intensive applications. Reconfigurable hardware devices in the form of Filed-Programmable Gate Arrays (FPGAs) have been proposed as viable system building blocks in the construction of high performance systems at an economical price. Given the importance and the use of matrix algorithms in scientific computing applications, they seem ideal candidates to harness and exploit the advantages offered by FPGAs. In this paper, a system for matrix algorithm cores generation is described. The system provides a catalog of efficient user-customizable cores, designed for FPGA implementation, ranging in three different matrix algorithm categories: (i) matrix operations, (ii) matrix transforms and (iii) matrix decomposition. The generated core can be either a general purpose or a specific application core. The methodology used in the design and implementation of two specific image processing application cores is presented. The first core is a fully pipelined matrix multiplier for colour space conversion based on distributed arithmetic principles while the second one is a parallel floating-point matrix multiplier designed for 3D affine transformations.Peer reviewe